Computer-assisted arthroplasty system

ABSTRACT

A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Applications62/801,245 (filed Feb. 5, 2019), 62/801,257 (filed Feb. 5, 2019),62/864,663 (filed Jun. 21, 2019), 62/885,673 (filed Aug. 12, 2019), and62/939,946 (filed Nov. 25, 2019), which are incorporated herein in theirentirety.

TECHNOLOGY FIELD

The present disclosure relates generally to methods, systems, andapparatuses related to a computer-assisted surgical system that includesvarious hardware and software components that work together to enhancesurgical workflows. The disclosed techniques may be applied to, forexample, shoulder, hip, and knee arthroplasties.

BACKGROUND

Common types of arthroplasty, such as partial knee arthroplasty (PKA),total knee arthroplasty (TKA), or total hip arthroplasty (THA) utilize asurgical plan to define one or more predefined cutting planes to resectbone to accommodate the implantation orientation and position (pose) ofa knee or hip implant/replacement joint. By resecting bone in accordancewith the surgical plan, patient bone can be shaped to a normalized,planned manner to accept a joint replacement implant with a given pose.The exact orientation and position of the joint replacement implant istypically planned according to a surgical plan developed beforecommencing surgery. However, a surgeon will often modify the plan in thesurgical theater based on information gathered about the patient'sjoint. Various systems exist to improve the surgical plan and workflow,yet there remains room for improvement.

SUMMARY

Embodiments of the present invention address and overcome one or more ofthe above shortcomings and drawbacks, by providing methods, systems, andapparatuses related to activity-optimized cutting guides for kneearthroplasty.

According to some embodiments, a computer-implemented method forcreating an activity-optimized cutting guides for surgical proceduresincludes receiving one or more pre-operative images depicting one ormore anatomical joints of a patient, and creating a three-dimensionalanatomical model of the one or more anatomical joints based on the oneor more pre-operative images. One or more patient-specific anatomicalmeasurements are determined based on the three-dimensional anatomicalmodel. A statistical model of joint performance is applied to thepatient-specific anatomical measurements to identify one or more cutangles for performing a surgical procedure. A patient-specific cuttingguide is created that comprises one or more apertures positioned basedon the one or more cut angles. For example, in some embodiments,creating the patient-specific cutting guide includes modifying acomputer model of a cutting guide blank to include the one or moreapertures. The computer model of the modified cutting guide blank isprovided to a device configured to manufacture the patient-specificcutting guide based on the computer model.

Various enhancements, refinements, and other modifications may be madeto the aforementioned method in different embodiments. For example, insome embodiments, the one or more patient-specific anatomicalmeasurements comprise distal and anterior condyle radii measurements. Inother embodiments, identifying one or more cut angles comprisesidentifying one or more cut angles that provide balanced condylar gapsthroughout a range of motion associated with one or more physicalactivities. In some embodiments, the statistical model of jointperformance comprises a plurality of transfer functions. In someembodiments, the statistical model of joint performance is applied tothe one or more patient-specific anatomical measurements by applying aMonte Carlo method to iteratively evaluate a plurality of possible cutangles. Each iteration of the Monte Carlo method applies the one or moretransfer functions with a distinct set of parameters. In someembodiments, the statistical model of joint performance is a machinelearning model.

In some embodiments, the aforementioned method (with or without themodifications discussed above) further includes generating thestatistical model by first selecting a plurality of implants. Eachimplant is defined by a distinct set of implantation features. Theimplantation features may include, for example, one or more of animplant position, an implant orientation, and an implant type. Adatabase of simulation results is populated by simulating a motion of aplurality of joint geometries while performing one or more physicalactivities and an expected stress on the plurality of implants resultingfrom the motion. The statistical model of joint performance is thengenerated based on the simulation results.

According to another aspect of the present invention, an article ofmanufacture for creating an activity-optimized cutting guide for asurgical procedure comprises a non-transitory computer-readable storagemedium storing computer-executable instructions for performing themethod discussed above (with or without the modifications discussedabove).

According to other embodiments, a system for creating anactivity-optimized cutting guide for a surgical procedure comprises adatabase and a computing system comprising one or more processors. Thedatabase includes one or more pre-operative images of one or moreanatomical joints of a patient. The computing system creates athree-dimensional anatomical model of the one or more anatomical jointsbased on the one or more pre-operative images, and determines one ormore patient-specific anatomical measurements based on thethree-dimensional anatomical model. These patient-specific anatomicalmeasurements may include, for example, distal and anterior condyle radiimeasurements. The computing system further applies a statistical modelof joint performance to the one or more patient-specific anatomicalmeasurements, and identifies one or more cut angles for performing asurgical procedure based on the application of the statistical model.For example, the cut angles could be identified such they providebalanced condylar gaps throughout a range of motion associated with oneor more physical activities. The computing system can then create apatient-specific cutting guide comprising one or more aperturespositioned based on the one or more cut angles. In some embodiments, thesystem further includes a manufacturing device configured to manufacturethe patient-specific cutting guide design. This manufacturing device maybe, for example, a 3D printer.

Additional features and advantages of the invention will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are illustrated by way of example in the figures ofthe accompanying drawings. Such embodiments are demonstrative and notintended to be exhaustive or exclusive embodiments of the presentsubject matter.

FIG. 1 illustrates an exemplary computer assisted surgical system foruse with some embodiments;

FIG. 2A illustrates examples of some of the control instructions thatmay be used by a surgical computer, in accordance with some embodiments;

FIG. 2B illustrates examples of some of the data that may be used by asurgical computer, in accordance with some embodiments;

FIG. 2C is a system diagram illustrating an example of a cloud-basedsystem used by a surgical computer, in accordance with some embodiments;

FIG. 3A provides a high-level overview of how recommendations can begenerated by a surgical computer, in accordance with some embodiments;

FIG. 3B shows an exemplary Implant Placement Interface, in accordancewith some embodiments;

FIG. 3C shows an exemplary Gap Planning Interface, in accordance withsome embodiments;

FIG. 3D shows an exemplary Optimization Parameterization Interface, inaccordance with some embodiments;

FIG. 3E shows an exemplary Response and Rationale Interface, inaccordance with some embodiments;

FIG. 4 provides a system diagram illustrating how optimization ofsurgical parameters can be performed, according to some embodiments;

FIGS. 5A-F provide an overview of a knee predictor equation, inaccordance with some embodiments;

FIG. 6 is a flow chart illustrating a process by which the optimizationof an equation set may be performed, according to some embodiments;

FIGS. 7A-1, 7A-2, 7A-3, and 7B provide an overview of an exemplary userinterface, for use with some embodiments;

FIG. 8 provides an overview of an exemplary Operative Patient CareSystem, for use with some embodiments;

FIG. 9 provides an overview of or a machine learning algorithm, for usewith some embodiments;

FIG. 10 is a flow chart illustrating operation of an exemplary OperativePatient Care System, for use with some embodiments;

FIGS. 11A-B are flow charts illustrating operation of an exemplaryOperative Patient Care System, for use with some embodiments;

FIGS. 11C-1, 11C-2, and 11C-3 provide an overview of an exemplary userinterface for implant placement, for use with some embodiments;

FIGS. 12A-12C provide some outputs that the anatomical modeling softwaremay use to visually depict the results of modeling hip activity, for usewith some embodiments;

FIG. 12D provides an exemplary visualization of a hip implant, for usewith some embodiments;

FIG. 13 provides an example of an augmented reality visualization insome embodiments;

FIG. 14 is a system diagram illustrating an augmented realityvisualization system, for use with some embodiments;

FIG. 15 is a flow chart illustrating operation of an augmented realitysystem, for use with some embodiments;

FIG. 16 is a system diagram illustrating an augmented realityvisualization system, for use with some embodiments;

FIG. 17A provides an example of an augmented reality visualization insome embodiments;

FIG. 17B provides an example of a three-dimensional visualization insome embodiments;

FIG. 18A provides an example of a three-dimensional visualization insome embodiments;

FIG. 18B provides an example of a three-dimensional visualization insome embodiments;

FIG. 19 provides an example of a three-dimensional model of a kneecomponents visualization, for use with some embodiments;

FIG. 20 is a system diagram illustrating an exemplary computationalsystem, for use with some embodiments;

FIG. 21 is a system diagram illustrating an exemplary computationalsystem, for use with some embodiments;

FIG. 22 is an anatomical diagram of hip geometry that may be used insome embodiments;

FIGS. 23A-B are diagrams of hip geometry within an x-ray image that maybe used in some embodiments;

FIG. 24 is a flow chart illustrating an exemplary process to extractanatomical landmarks and determine a surgical plan based on modeledperformance, for use with some embodiments;

FIG. 25 is a table depicting exemplary values for hip geometry, for usewith some embodiments;

FIGS. 26A-D are exemplary user interfaces, for use with someembodiments;

FIG. 27 depicts an exemplary combination of model results for differentselected activities for a given geometry to display an aggregate result;

FIG. 28 is a flow chart illustrating an exemplary method for creating astatistical model database to help determine a surgical plan based onmodeled performance, for use with some embodiments;

FIG. 29 is a flow chart illustrating an exemplary method for creating asurgical plan using a statistical model database based on modeledperformance, for use with some embodiments;

FIG. 30 is a flow chart illustrating an exemplary method for modifying asurgical plan using a statistical model database based on modeledperformance, for use with some embodiments;

FIG. 31 is a pair of annotated x-ray images showing exemplary kneegeometry that can be used in some embodiments;

FIG. 32 is system diagram for an exemplary embodiment of a surgicalsystem, for use with some embodiments;

FIG. 33 is a view of a surgical scene using some of the techniquesdisclosed herein; and

FIGS. 34A-B illustrates the process of measurement of a patient knee atvarious degrees of flexion.

DETAILED DESCRIPTION

This disclosure is not limited to the particular systems, devices andmethods described, as these may vary. The terminology used in thedescription is for the purpose of describing the particular versions orembodiments only, and is not intended to limit the scope.

As used in this document, the singular forms “a,” “an,” and “the”include plural references unless the context clearly dictates otherwise.Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of ordinary skillin the art. Nothing in this disclosure is to be construed as anadmission that the embodiments described in this disclosure are notentitled to antedate such disclosure by virtue of prior invention. Asused in this document, the term “comprising” means “including, but notlimited to.”

The disclosed devices are particularly well adapted for surgicalprocedures that utilize surgical navigation systems, such as the NAVIO®surgical navigation system. Such procedures can include knee replacementand/or revision surgery, as well as shoulder and hip surgeries. NAVIO isa registered trademark of BLUE BELT TECHNOLOGIES, INC. of Pittsburgh,Pa., which is a subsidiary of SMITH & NEPHEW, INC. of Memphis, Tenn.

Definitions

For the purposes of this disclosure, the term “implant” is used to referto a prosthetic device or structure manufactured to replace or enhance abiological structure. For example, in a total hip replacement procedurea prosthetic acetabular cup (implant) is used to replace or enhance apatient's worn or damaged acetabulum. While the term “implant” isgenerally considered to denote a man-made structure (as contrasted witha transplant), for the purposes of this specification an implant caninclude a biological tissue or material transplanted to replace orenhance a biological structure.

For the purposes of this disclosure, the term “real-time” is used torefer to calculations or operations performed on-the-fly as events occuror input is received by the operable system. However, the use of theterm “real-time” is not intended to preclude operations that cause somelatency between input and response, so long as the latency is anunintended consequence induced by the performance characteristics of themachine.

Although much of this disclosure refers to surgeons or other medicalprofessionals by specific job title or role, nothing in this disclosureis intended to be limited to a specific job title or function. Surgeonsor medical professionals can include any doctor, nurse, medicalprofessional, or technician. Any of these terms or job titles can beused interchangeably with the user of the systems disclosed hereinunless otherwise explicitly demarcated. For example, a reference to asurgeon could also apply, in some embodiments to a technician or nurse.

CASS Ecosystem Overview

FIG. 1 provides an illustration of an example computer-assisted surgicalsystem (CASS) 100, according to some embodiments. As described infurther detail in the sections that follow, the CASS uses computers,robotics, and imaging technology to aid surgeons in performingorthopedic surgery procedures such as total knee arthroplasty (TKA) ortotal hip arthroplasty (THA). For example, surgical navigation systemscan aid surgeons in locating patient anatomical structures, guidingsurgical instruments, and implanting medical devices with a high degreeof accuracy. Surgical navigation systems such as the CASS 100 oftenemploy various forms of computing technology to perform a wide varietyof standard and minimally invasive surgical procedures and techniques.Moreover, these systems allow surgeons to more accurately plan, trackand navigate the placement of instruments and implants relative to thebody of a patient, as well as conduct pre-operative and intra-operativebody imaging.

An Effector Platform 105 positions surgical tools relative to a patientduring surgery. The exact components of the Effector Platform 105 willvary, depending on the embodiment employed. For example, for a kneesurgery, the Effector Platform 105 may include an End Effector 105B thatholds surgical tools or instruments during their use. The End Effector105B may be a handheld device or instrument used by the surgeon (e.g., aNAVIO® hand piece or a cutting guide or jig) or, alternatively, the EndEffector 105B can include a device or instrument held or positioned by aRobotic Arm 105A. While one Robotic Arm 105A is illustrated in FIG. 1,in some embodiments there may be multiple devices. As examples, theremay be one Robotic Arm 105A on each side of an operating table or twodevices on one side of the table. The Robotic Arm 105A may be mounteddirectly to the table, be located next to the table on a floor platform(not shown), mounted on a floor-to-ceiling pole, or mounted on a wall orceiling of an operating room. The floor platform may be fixed ormoveable. In one particular embodiment, the robotic arm 105A is mountedon a floor-to-ceiling pole located between the patient's legs or feet.In some embodiments, the End Effector 105B may include a suture holderor a stapler to assist in closing wounds. Further, in the case of tworobotic arms 105A, the surgical computer 150 can drive the robotic arms105A to work together to suture the wound at closure. Alternatively, thesurgical computer 150 can drive one or more robotic arms 105A to staplethe wound at closure.

The Effector Platform 105 can include a Limb Positioner 105C forpositioning the patient's limbs during surgery. One example of a LimbPositioner 105C is the SMITH AND NEPHEW SPIDER2™ system. The LimbPositioner 105C may be operated manually by the surgeon or alternativelychange limb positions based on instructions received from the SurgicalComputer 150 (described below). While one Limb Positioner 105C isillustrated in FIG. 1, in some embodiments there may be multipledevices. As examples, there may be one Limb Positioner 105C on each sideof the operating table or two devices on one side of the table. The LimbPositioner 105C may be mounted directly to the table, be located next tothe table on a floor platform (not shown), mounted on a pole, or mountedon a wall or ceiling of an operating room. In some embodiments, the LimbPositioner 105C can be used in non-conventional ways, such as aretractor or specific bone holder. The Limb Positioner 105C may include,as examples, an ankle boot, a soft tissue clamp, a bone clamp, or asoft-tissue retractor spoon, such as a hooked, curved, or angled blade.In some embodiments, the Limb Positioner 105C may include a sutureholder to assist in closing wounds.

The Effector Platform 105 may include tools, such as a screwdriver,light or laser, to indicate an axis or plane, bubble level, pin driver,pin puller, plane checker, pointer, finger, or some combination thereof.

Resection Equipment 110 (not shown in FIG. 1) performs bone or tissueresection using, for example, mechanical, ultrasonic, or lasertechniques. Examples of Resection Equipment 110 include drillingdevices, burring devices, oscillatory sawing devices, vibratoryimpaction devices, reamers, ultrasonic bone cutting devices, radiofrequency ablation devices, reciprocating devices (such as a rasp orbroach), and laser ablation systems. In some embodiments, the ResectionEquipment 110 is held and operated by the surgeon during surgery. Inother embodiments, the Effector Platform 105 may be used to hold theResection Equipment 110 during use.

The Effector Platform 105 can also include a cutting guide or jig 105Dthat is used to guide saws or drills used to resect tissue duringsurgery. Such cutting guides 105D can be formed integrally as part ofthe Effector Platform 105 or Robotic Arm 105A, or cutting guides can beseparate structures that can be matingly and/or removably attached tothe Effector Platform 105 or Robotic Arm 105A. The Effector Platform 105or Robotic Arm 105A can be controlled by the CASS 100 to position acutting guide or jig 105D adjacent to the patient's anatomy inaccordance with a pre-operatively or intraoperatively developed surgicalplan such that the cutting guide or jig will produce a precise bone cutin accordance with the surgical plan.

The Tracking System 115 uses one or more sensors to collect real-timeposition data that locates the patient's anatomy and surgicalinstruments. For example, for TKA procedures, the Tracking System 115may provide a location and orientation of the End Effector 105B duringthe procedure. In addition to positional data, data from the TrackingSystem 115 can also be used to infer velocity/acceleration ofanatomy/instrumentation, which can be used for tool control. In someembodiments, the Tracking System 115 may use a tracker array attached tothe End Effector 105B to determine the location and orientation of theEnd Effector 105B. The position of the End Effector 105B may be inferredbased on the position and orientation of the Tracking System 115 and aknown relationship in three-dimensional space between the TrackingSystem 115 and the End Effector 105B. Various types of tracking systemsmay be used in various embodiments of the present invention including,without limitation, Infrared (IR) tracking systems, electromagnetic (EM)tracking systems, video or image based tracking systems, and ultrasoundregistration and tracking systems. Using the data provided by thetracking system 115, the surgical computer 150 can detect objects andprevent collision. For example, the surgical computer 150 can preventthe Robotic Arm 105A from colliding with soft tissue.

Any suitable tracking system can be used for tracking surgical objectsand patient anatomy in the surgical theatre. For example, a combinationof IR and visible light cameras can be used in an array. Variousillumination sources, such as an IR LED light source, can illuminate thescene allowing three-dimensional imaging to occur. In some embodiments,this can include stereoscopic, tri-scopic, quad-scopic, etc., imaging.In addition to the camera array, which in some embodiments is affixed toa cart, additional cameras can be placed throughout the surgicaltheatre. For example, handheld tools or headsets worn byoperators/surgeons can include imaging capability that communicatesimages back to a central processor to correlate those images with imagescaptured by the camera array. This can give a more robust image of theenvironment for modeling using multiple perspectives. Furthermore, someimaging devices may be of suitable resolution or have a suitableperspective on the scene to pick up information stored in quick response(QR) codes or barcodes. This can be helpful in identifying specificobjects not manually registered with the system. In some embodiments,the camera may be mounted on the Robotic Arm 105A.

In some embodiments, specific objects can be manually registered by asurgeon with the system preoperatively or intraoperatively. For example,by interacting with a user interface, a surgeon may identify thestarting location for a tool or a bone structure. By tracking fiducialmarks associated with that tool or bone structure, or by using otherconventional image tracking modalities, a processor may track that toolor bone as it moves through the environment in a three-dimensionalmodel.

In some embodiments, certain markers, such as fiducial marks thatidentify individuals, important tools, or bones in the theater mayinclude passive or active identifiers that can be picked up by a cameraor camera array associated with the tracking system. For example, an IRLED can flash a pattern that conveys a unique identifier to the sourceof that pattern, providing a dynamic identification mark. Similarly, oneor two dimensional optical codes (barcode, QR code, etc.) can be affixedto objects in the theater to provide passive identification that canoccur based on image analysis. If these codes are placed asymmetricallyon an object, they can also be used to determine an orientation of anobject by comparing the location of the identifier with the extents ofan object in an image. For example, a QR code may be placed in a cornerof a tool tray, allowing the orientation and identity of that tray to betracked. Other tracking modalities are explained throughout. Forexample, in some embodiments, augmented reality headsets can be worn bysurgeons and other staff to provide additional camera angles andtracking capabilities.

In addition to optical tracking, certain features of objects can betracked by registering physical properties of the object and associatingthem with objects that can be tracked, such as fiducial marks fixed to atool or bone. For example, a surgeon may perform a manual registrationprocess whereby a tracked tool and a tracked bone can be manipulatedrelative to one another. By impinging the tip of the tool against thesurface of the bone, a three-dimensional surface can be mapped for thatbone that is associated with a position and orientation relative to theframe of reference of that fiducial mark. By optically tracking theposition and orientation (pose) of the fiducial mark associated withthat bone, a model of that surface can be tracked with an environmentthrough extrapolation.

The registration process that registers the CASS 100 to the relevantanatomy of the patient can also involve the use of anatomical landmarks,such as landmarks on a bone or cartilage. For example, the CASS 100 caninclude a 3D model of the relevant bone or joint and the surgeon canintraoperatively collect data regarding the location of bony landmarkson the patient's actual bone using a probe that is connected to theCASS. Bony landmarks can include, for example, the medial malleolus andlateral malleolus, the ends of the proximal femur and distal tibia, andthe center of the hip joint. The CASS 100 can compare and register thelocation data of bony landmarks collected by the surgeon with the probewith the location data of the same landmarks in the 3D model.Alternatively, the CASS 100 can construct a 3D model of the bone orjoint without pre-operative image data by using location data of bonylandmarks and the bone surface that are collected by the surgeon using aCASS probe or other means. The registration process can also includedetermining various axes of a joint. For example, for a TKA the surgeoncan use the CASS 100 to determine the anatomical and mechanical axes ofthe femur and tibia. The surgeon and the CASS 100 can identify thecenter of the hip joint by moving the patient's leg in a spiraldirection (i.e., circumduction) so the CASS can determine where thecenter of the hip joint is located.

A Tissue Navigation System 120 (not shown in FIG. 1) provides thesurgeon with intraoperative, real-time visualization for the patient'sbone, cartilage, muscle, nervous, and/or vascular tissues surroundingthe surgical area. Examples of systems that may be employed for tissuenavigation include fluorescent imaging systems and ultrasound systems.

The Display 125 provides graphical user interfaces (GUIs) that displayimages collected by the Tissue Navigation System 120 as well otherinformation relevant to the surgery. For example, in one embodiment, theDisplay 125 overlays image information collected from various modalities(e.g., CT, MRI, X-ray, fluorescent, ultrasound, etc.) collectedpre-operatively or intra-operatively to give the surgeon various viewsof the patient's anatomy as well as real-time conditions. The Display125 may include, for example, one or more computer monitors. As analternative or supplement to the Display 125, one or more members of thesurgical staff may wear an Augmented Reality (AR) Head Mounted Device(HMD). For example, in FIG. 1 the Surgeon 111 is wearing an AR HMD 155that may, for example, overlay pre-operative image data on the patientor provide surgical planning suggestions. Various example uses of the ARHMD 155 in surgical procedures are detailed in the sections that follow.

Surgical Computer 150 provides control instructions to variouscomponents of the CASS 100, collects data from those components, andprovides general processing for various data needed during surgery. Insome embodiments, the Surgical Computer 150 is a general purposecomputer. In other embodiments, the Surgical Computer 150 may be aparallel computing platform that uses multiple central processing units(CPUs) or graphics processing units (GPU) to perform processing. In someembodiments, the Surgical Computer 150 is connected to a remote serverover one or more computer networks (e.g., the Internet). The remoteserver can be used, for example, for storage of data or execution ofcomputationally intensive processing tasks.

Various techniques generally known in the art can be used for connectingthe Surgical Computer 150 to the other components of the CASS 100.Moreover, the computers can connect to the Surgical Computer 150 using amix of technologies. For example, the End Effector 105B may connect tothe Surgical Computer 150 over a wired (i.e., serial) connection. TheTracking System 115, Tissue Navigation System 120, and Display 125 cansimilarly be connected to the Surgical Computer 150 using wiredconnections. Alternatively, the Tracking System 115, Tissue NavigationSystem 120, and Display 125 may connect to the Surgical Computer 150using wireless technologies such as, without limitation, Wi-Fi,Bluetooth, Near Field Communication (NFC), or ZigBee.

Powered Impaction and Acetabular Reamer Devices

Part of the flexibility of the CASS design described above with respectto FIG. 1 is that additional or alternative devices can be added to theCASS 100 as necessary to support particular surgical procedures. Forexample, in the context of hip surgeries, the CASS 100 may include apowered impaction device. Impaction devices are designed to repeatedlyapply an impaction force that the surgeon can use to perform activitiessuch as implant alignment. For example, within a total hip arthroplasty(THA), a surgeon will often insert a prosthetic acetabular cup into theimplant host's acetabulum using an impaction device. Although impactiondevices can be manual in nature (e.g., operated by the surgeon strikingan impactor with a mallet), powered impaction devices are generallyeasier and quicker to use in the surgical setting. Powered impactiondevices may be powered, for example, using a battery attached to thedevice. Various attachment pieces may be connected to the poweredimpaction device to allow the impaction force to be directed in variousways as needed during surgery. Also in the context of hip surgeries, theCASS 100 may include a powered, robotically controlled end effector toream the acetabulum to accommodate an acetabular cup implant.

In a robotically-assisted THA, the patient's anatomy can be registeredto the CASS 100 using CT or other image data, the identification ofanatomical landmarks, tracker arrays attached to the patient's bones,and one or more cameras. Tracker arrays can be mounted on the iliaccrest using clamps and/or bone pins and such trackers can be mountedexternally through the skin or internally (either posterolaterally oranterolaterally) through the incision made to perform the THA. For aTHA, the CASS 100 can utilize one or more femoral cortical screwsinserted into the proximal femur as checkpoints to aid in theregistration process. The CASS 100 can also utilize one or morecheckpoint screws inserted into the pelvis as additional checkpoints toaid in the registration process. Femoral tracker arrays can be securedto or mounted in the femoral cortical screws. The CASS 100 can employsteps where the registration is verified using a probe that the surgeonprecisely places on key areas of the proximal femur and pelvisidentified for the surgeon on the display 125. Trackers can be locatedon the robotic arm 105A or end effector 105B to register the arm and/orend effector to the CASS 100. The verification step can also utilizeproximal and distal femoral checkpoints. The CASS 100 can utilize colorprompts or other prompts to inform the surgeon that the registrationprocess for the relevant bones and the robotic arm 105A or end effector105B has been verified to a certain degree of accuracy (e.g., within 1mm).

For a THA, the CASS 100 can include a broach tracking option usingfemoral arrays to allow the surgeon to intraoperatively capture thebroach position and orientation and calculate hip length and offsetvalues for the patient. Based on information provided about thepatient's hip joint and the planned implant position and orientationafter broach tracking is completed, the surgeon can make modificationsor adjustments to the surgical plan.

For a robotically-assisted THA, the CASS 100 can include one or morepowered reamers connected or attached to a robotic arm 105A or endeffector 105B that prepares the pelvic bone to receive an acetabularimplant according to a surgical plan. The robotic arm 105A and/or endeffector 105B can inform the surgeon and/or control the power of thereamer to ensure that the acetabulum is being resected (reamed) inaccordance with the surgical plan. For example, if the surgeon attemptsto resect bone outside of the boundary of the bone to be resected inaccordance with the surgical plan, the CASS 100 can power off the reameror instruct the surgeon to power off the reamer. The CASS 100 canprovide the surgeon with an option to turn off or disengage the roboticcontrol of the reamer. The display 125 can depict the progress of thebone being resected (reamed) as compared to the surgical plan usingdifferent colors. The surgeon can view the display of the bone beingresected (reamed) to guide the reamer to complete the reaming inaccordance with the surgical plan. The CASS 100 can provide visual oraudible prompts to the surgeon to warn the surgeon that resections arebeing made that are not in accordance with the surgical plan.

Following reaming, the CASS 100 can employ a manual or powered impactorthat is attached or connected to the robotic arm 105A or end effector105B to impact trial implants and final implants into the acetabulum.The robotic arm 105A and/or end effector 105B can be used to guide theimpactor to impact the trial and final implants into the acetabulum inaccordance with the surgical plan. The CASS 100 can cause the positionand orientation of the trial and final implants vis-à-vis the bone to bedisplayed to inform the surgeon as to how the trial and final implant'sorientation and position compare to the surgical plan, and the display125 can show the implant's position and orientation as the surgeonmanipulates the leg and hip. The CASS 100 can provide the surgeon withthe option of re-planning and re-doing the reaming and implant impactionby preparing a new surgical plan if the surgeon is not satisfied withthe original implant position and orientation.

Preoperatively, the CASS 100 can develop a proposed surgical plan basedon a three dimensional model of the hip joint and other informationspecific to the patient, such as the mechanical and anatomical axes ofthe leg bones, the epicondylar axis, the femoral neck axis, thedimensions (e.g., length) of the femur and hip, the midline axis of thehip joint, the ASIS axis of the hip joint, and the location ofanatomical landmarks such as the lesser trochanter landmarks, the distallandmark, and the center of rotation of the hip joint. TheCASS-developed surgical plan can provide a recommended optimal implantsize and implant position and orientation based on the three dimensionalmodel of the hip joint and other information specific to the patient.The CASS-developed surgical plan can include proposed details on offsetvalues, inclination and anteversion values, center of rotation, cupsize, medialization values, superior-inferior fit values, femoral stemsizing and length.

For a THA, the CASS-developed surgical plan can be viewed preoperativelyand intraoperatively, and the surgeon can modify CASS-developed surgicalplan preoperatively or intraoperatively. The CASS-developed surgicalplan can display the planned resection to the hip joint and superimposethe planned implants onto the hip joint based on the planned resections.The CASS 100 can provide the surgeon with options for different surgicalworkflows that will be displayed to the surgeon based on a surgeon'spreference. For example, the surgeon can choose from different workflowsbased on the number and types of anatomical landmarks that are checkedand captured and/or the location and number of tracker arrays used inthe registration process.

According to some embodiments, a powered impaction device used with theCASS 100 may operate with a variety of different settings. In someembodiments, the surgeon adjusts settings through a manual switch orother physical mechanism on the powered impaction device. In otherembodiments, a digital interface may be used that allows setting entry,for example, via a touchscreen on the powered impaction device. Such adigital interface may allow the available settings to vary based, forexample, on the type of attachment piece connected to the powerattachment device. In some embodiments, rather than adjusting thesettings on the powered impaction device itself, the settings can bechanged through communication with a robot or other computer systemwithin the CASS 100. Such connections may be established using, forexample, a Bluetooth or Wi-Fi networking module on the powered impactiondevice. In another embodiment, the impaction device and end pieces maycontain features that allow the impaction device to be aware of what endpiece (cup impactor, broach handle, etc.) is attached with no actionrequired by the surgeon, and adjust the settings accordingly. This maybe achieved, for example, through a QR code, barcode, RFID tag, or othermethod.

Examples of the settings that may be used include cup impaction settings(e.g., single direction, specified frequency range, specified forceand/or energy range); broach impaction settings (e.g., dualdirection/oscillating at a specified frequency range, specified forceand/or energy range); femoral head impaction settings (e.g., singledirection/single blow at a specified force or energy); and stemimpaction settings (e.g., single direction at specified frequency with aspecified force or energy). Additionally, in some embodiments, thepowered impaction device includes settings related to acetabular linerimpaction (e.g., single direction/single blow at a specified force orenergy). There may be a plurality of settings for each type of linersuch as poly, ceramic, oxinium, or other materials. Furthermore, thepowered impaction device may offer settings for different bone qualitybased on preoperative testing/imaging/knowledge and/or intraoperativeassessment by the surgeon. In some embodiments, the powered impactordevice may have a dual function. For example, the powered impactordevice not only could provide reciprocating motion to provide an impactforce, but also could provide reciprocating motion for a broach or rasp.

In some embodiments, the powered impaction device includes feedbacksensors that gather data during instrument use, and send data to acomputing device such as a controller within the device or the SurgicalComputer 150. This computing device can then record the data for lateranalysis and use. Examples of the data that may be collected include,without limitation, sound waves, the predetermined resonance frequencyof each instrument, reaction force or rebound energy from patient bone,location of the device with respect to imaging (e.g., fluoro, CT,ultrasound, MRI, etc.) registered bony anatomy, and/or external straingauges on bones.

Once the data is collected, the computing device may execute one or morealgorithms in real-time or near real-time to aid the surgeon inperforming the surgical procedure. For example, in some embodiments, thecomputing device uses the collected data to derive information such asthe proper final broach size (femur); when the stem is fully seated(femur side); or when the cup is seated (depth and/or orientation) for aTHA. Once the information is known, it may be displayed for thesurgeon's review, or it may be used to activate haptics or otherfeedback mechanisms to guide the surgical procedure.

Additionally, the data derived from the aforementioned algorithms may beused to drive operation of the device. For example, during insertion ofa prosthetic acetabular cup with a powered impaction device, the devicemay automatically extend an impaction head (e.g., an end effector)moving the implant into the proper location, or turn the power off tothe device once the implant is fully seated. In one embodiment, thederived information may be used to automatically adjust settings forquality of bone where the powered impaction device should use less powerto mitigate femoral/acetabular/pelvic fracture or damage to surroundingtissues.

Robotic Arm

In some embodiments, the CASS 100 includes a robotic arm 105A thatserves as an interface to stabilize and hold a variety of instrumentsused during the surgical procedure. For example, in the context of a hipsurgery, these instruments may include, without limitation, retractors,a sagittal or reciprocating saw, the reamer handle, the cup impactor,the broach handle, and the stem inserter. The robotic arm 105A may havemultiple degrees of freedom (like a Spider device), and have the abilityto be locked in place (e.g., by a press of a button, voice activation, asurgeon removing a hand from the robotic arm, or other method).

In some embodiments, movement of the robotic arm 105A may be effectuatedby use of a control panel built into the robotic arm system. Forexample, a display screen may include one or more input sources, such asphysical buttons or a user interface having one or more icons, thatdirect movement of the robotic arm 105A. The surgeon or other healthcareprofessional may engage with the one or more input sources to positionthe robotic arm 105A when performing a surgical procedure.

A tool or an end effector 105B may be attached or integrated into arobotic arm 105A which may include, without limitation, a burringdevice, a scalpel, a cutting device, a retractor, a joint tensioningdevice, or the like. In embodiments in which an end effector 105B isused, the end effector 105B may be positioned at the end of the roboticarm 105A such that any motor control operations are performed within therobotic arm system. In embodiments in which a tool is used, the tool maybe secured at a distal end of the robotic arm 105A, but motor controloperation may reside within the tool itself.

The robotic arm 105A may be motorized internally to both stabilize therobotic arm, thereby preventing it from falling and hitting the patient,surgical table, surgical staff, etc., and to allow the surgeon to movethe robotic arm without having to fully support its weight. While thesurgeon is moving the robotic arm 105A, the robotic arm may provide someresistance to prevent the robotic arm from moving too fast or having toomany degrees of freedom active at once. The position and the lock statusof the robotic arm 105A may be tracked, for example, by a controller orthe Surgical Computer 150.

In some embodiments, the robotic arm 105A can be moved by hand (e.g., bythe surgeon) or with internal motors into its ideal position andorientation for the task being performed. In some embodiments, therobotic arm 105A may be enabled to operate in a “free” mode that allowsthe surgeon to position the arm into a desired position without beingrestricted. While in the free mode, the position and orientation of therobotic arm 105A may still be tracked as described above. In oneembodiment, certain degrees of freedom can be selectively released uponinput from user (e.g., surgeon) during specified portions of thesurgical plan tracked by the Surgical Computer 150. Designs in which arobotic arm 105A is internally powered through hydraulics or motors orprovides resistance to external manual motion through similar means canbe described as powered robotic arms, while arms that are manuallymanipulated without power feedback, but which may be manually orautomatically locked in place, may be described as passive robotic arms.

A robotic arm 105A or end effector 105B can include a trigger or othermeans to control the power of a saw or drill. Engagement of the triggeror other means by the surgeon can cause the robotic arm 105A or endeffector 105B to transition from a motorized alignment mode to a modewhere the saw or drill is engaged and powered on. Additionally, the CASS100 can include a foot pedal (not shown) that causes the system toperform certain functions when activated. For example, the surgeon canactivate the foot pedal to instruct the CASS 100 to place the roboticarm 105A or end effector 105B in an automatic mode that brings therobotic arm or end effector into the proper position with respect to thepatient's anatomy in order to perform the necessary resections. The CASS100 can also place the robotic arm 105A or end effector 105B in acollaborative mode that allows the surgeon to manually manipulate andposition the robotic arm or end effector into a particular location. Thecollaborative mode can be configured to allow the surgeon to move therobotic arm 105A or end effector 105B medially or laterally, whilerestricting movement in other directions. As discussed, the robotic arm105A or end effector 105B can include a cutting device (saw, drill, andburr) or a cutting guide or jig 105D that will guide a cutting device.In other embodiments, movement of the robotic arm 105A or roboticallycontrolled end effector 105B can be controlled entirely by the CASS 100without any, or with only minimal, assistance or input from a surgeon orother medical professional. In still other embodiments, the movement ofthe robotic arm 105A or robotically controlled end effector 105B can becontrolled remotely by a surgeon or other medical professional using acontrol mechanism separate from the robotic arm or roboticallycontrolled end effector device, for example, using a joystick orinteractive monitor or display control device.

The examples below describe uses of the robotic device in the context ofa hip surgery; however, it should be understood that the robotic arm mayhave other applications for surgical procedures involving knees,shoulders, etc. One example of use of a robotic arm in the context offorming an anterior cruciate ligament (ACL) graft tunnel is described inPCT/US2019/048502 filed Aug. 28, 2019 and entitled “Robotic AssistedLigament Graft Placement and Tensioning,” the entirety of which isincorporated herein by reference.

A robotic arm 105A may be used for holding the retractor. For example inone embodiment, the robotic arm 105A may be moved into the desiredposition by the surgeon. At that point, the robotic arm 105A may lockinto place. In some embodiments, the robotic arm 105A is provided withdata regarding the patient's position, such that if the patient moves,the robotic arm can adjust the retractor position accordingly. In someembodiments, multiple robotic arms may be used, thereby allowingmultiple retractors to be held or for more than one activity to beperformed simultaneously (e.g., retractor holding & reaming).

The robotic arm 105A may also be used to help stabilize the surgeon'shand while making a femoral neck cut. In this application, control ofthe robotic arm 105A may impose certain restrictions to prevent softtissue damage from occurring. For example, in one embodiment, theSurgical Computer 150 tracks the position of the robotic arm 105A as itoperates. If the tracked location approaches an area where tissue damageis predicted, a command may be sent to the robotic arm 105A causing itto stop. Alternatively, where the robotic arm 105A is automaticallycontrolled by the Surgical Computer 150, the Surgical Computer mayensure that the robotic arm is not provided with any instructions thatcause it to enter areas where soft tissue damage is likely to occur. TheSurgical Computer 150 may impose certain restrictions on the surgeon toprevent the surgeon from reaming too far into the medial wall of theacetabulum or reaming at an incorrect angle or orientation.

In some embodiments, the robotic arm 105A may be used to hold a cupimpactor at a desired angle or orientation during cup impaction. Whenthe final position has been achieved, the robotic arm 105A may preventany further seating to prevent damage to the pelvis.

The surgeon may use the robotic arm 105A to position the broach handleat the desired position and allow the surgeon to impact the broach intothe femoral canal at the desired orientation. In some embodiments, oncethe Surgical Computer 150 receives feedback that the broach is fullyseated, the robotic arm 105A may restrict the handle to prevent furtheradvancement of the broach.

The robotic arm 105A may also be used for resurfacing applications. Forexample, the robotic arm 105A may stabilize the surgeon's hand whileusing traditional instrumentation and provide certain restrictions orlimitations to allow for proper placement of implant components (e.g.,guide wire placement, chamfer cutter, sleeve cutter, plan cutter, etc.).Where only a burr is employed, the robotic arm 105A may stabilize thesurgeon's handpiece and may impose restrictions on the handpiece toprevent the surgeon from removing unintended bone in contravention ofthe surgical plan.

The robotic arm 105A may be a passive arm. As an example, the roboticarm 105A may be a CIRQ robot arm available from Brainlab AG. CIRQ is aregistered trademark of Brainlab AG, Olof-Palme-Str. 9 81829, München,FED REP of GERMANY. In one particular embodiment, the robotic arm 105Ais an intelligent holding arm as disclosed in U.S. patent applicationSer. No. 15/525,585 to Krinninger et al., U.S. patent application Ser.No. 15/561,042 to Nowatschin et al., U.S. patent application Ser. No.15/561,048 to Nowatschin et al., and U.S. Pat. No. 10,342,636 toNowatschin et al., the entire contents of each of which is hereinincorporated by reference.

Surgical Procedure Data Generation and Collection

The various services that are provided by medical professionals to treata clinical condition are collectively referred to as an “episode ofcare.” For a particular surgical intervention the episode of care caninclude three phases: pre-operative, intra-operative, andpost-operative. During each phase, data is collected or generated thatcan be used to analyze the episode of care in order to understandvarious aspects of the procedure and identify patterns that may be used,for example, in training models to make decisions with minimal humanintervention. The data collected over the episode of care may be storedat the Surgical Computer 150 or the Surgical Data Server 180 (shown inFIG. 2C) as a complete dataset. Thus, for each episode of care, adataset exists that comprises all of the data collectivelypre-operatively about the patient, all of the data collected or storedby the CASS 100 intra-operatively, and any post-operative data providedby the patient or by a healthcare professional monitoring the patient.

As explained in further detail, the data collected during the episode ofcare may be used to enhance performance of the surgical procedure or toprovide a holistic understanding of the surgical procedure and thepatient outcomes. For example, in some embodiments, the data collectedover the episode of care may be used to generate a surgical plan. In oneembodiment, a high-level, pre-operative plan is refinedintra-operatively as data is collected during surgery. In this way, thesurgical plan can be viewed as dynamically changing in real-time or nearreal-time as new data is collected by the components of the CASS 100. Inother embodiments, pre-operative images or other input data may be usedto develop a robust plan preoperatively that is simply executed duringsurgery. In this case, the data collected by the CASS 100 during surgerymay be used to make recommendations that ensure that the surgeon stayswithin the pre-operative surgical plan. For example, if the surgeon isunsure how to achieve a certain prescribed cut or implant alignment, theSurgical Computer 150 can be queried for a recommendation. In stillother embodiments, the pre-operative and intra-operative planningapproaches can be combined such that a robust pre-operative plan can bedynamically modified, as necessary or desired, during the surgicalprocedure. In some embodiments, a biomechanics-based model of patientanatomy contributes simulation data to be considered by the CASS 100 indeveloping preoperative, intraoperative, andpost-operative/rehabilitation procedures to optimize implant performanceoutcomes for the patient.

Aside from changing the surgical procedure itself, the data gatheredduring the episode of care may be used as an input to other proceduresancillary to the surgery. For example, in some embodiments, implants canbe designed using episode of care data. Example data-driven techniquesfor designing, sizing, and fitting implants are described in U.S. patentapplication Ser. No. 13/814,531 filed Aug. 15, 2011 and entitled“Systems and Methods for Optimizing Parameters for OrthopaedicProcedures”; U.S. patent application Ser. No. 14/232,958 filed Jul. 20,2012 and entitled “Systems and Methods for Optimizing Fit of an Implantto Anatomy”; and U.S. patent application Ser. No. 12/234,444 filed Sep.19, 2008 and entitled “Operatively Tuning Implants for IncreasedPerformance,” the entire contents of each of which are herebyincorporated by reference into this patent application.

Furthermore, the data can be used for educational, training, or researchpurposes. For example, using the network-based approach described belowin FIG. 2C, other doctors or students can remotely view surgeries ininterfaces that allow them to selectively view data as it is collectedfrom the various components of the CASS 100. After the surgicalprocedure, similar interfaces may be used to “playback” a surgery fortraining or other educational purposes, or to identify the source of anyissues or complications with the procedure.

Data acquired during the pre-operative phase generally includes allinformation collected or generated prior to the surgery. Thus, forexample, information about the patient may be acquired from a patientintake form or electronic medical record (EMR). Examples of patientinformation that may be collected include, without limitation, patientdemographics, diagnoses, medical histories, progress notes, vital signs,medical history information, allergies, and lab results. Thepre-operative data may also include images related to the anatomicalarea of interest. These images may be captured, for example, usingMagnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray,ultrasound, or any other modality known in the art. The pre-operativedata may also comprise quality of life data captured from the patient.For example, in one embodiment, pre-surgery patients use a mobileapplication (“app”) to answer questionnaires regarding their currentquality of life. In some embodiments, preoperative data used by the CASS100 includes demographic, anthropometric, cultural, or other specifictraits about a patient that can coincide with activity levels andspecific patient activities to customize the surgical plan to thepatient. For example, certain cultures or demographics may be morelikely to use a toilet that requires squatting on a daily basis.

FIGS. 2A and 2B provide examples of data that may be acquired during theintra-operative phase of an episode of care. These examples are based onthe various components of the CASS 100 described above with reference toFIG. 1; however, it should be understood that other types of data may beused based on the types of equipment used during surgery and their use.

FIG. 2A shows examples of some of the control instructions that theSurgical Computer 150 provides to other components of the CASS 100,according to some embodiments. Note that the example of FIG. 2A assumesthat the components of the Effector Platform 105 are each controlleddirectly by the Surgical Computer 150. In embodiments where a componentis manually controlled by the Surgeon 111, instructions may be providedon the Display 125 or AR HMD 155 (as shown in FIG. 1) instructing theSurgeon 111 how to move the component.

The various components included in the Effector Platform 105 arecontrolled by the Surgical Computer 150 providing position commands thatinstruct the component where to move within a coordinate system. In someembodiments, the Surgical Computer 150 provides the Effector Platform105 with instructions defining how to react when a component of theEffector Platform 105 deviates from a surgical plan. These commands arereferenced in FIG. 2A as “haptic” commands. For example, the EndEffector 105B may provide a force to resist movement outside of an areawhere resection is planned. Other commands that may be used by theEffector Platform 105 include vibration and audio cues.

In some embodiments, the end effectors 105B of the robotic arm 105A areoperatively coupled with cutting guide 105D (as shown in FIG. 1). Inresponse to an anatomical model of the surgical scene, the robotic arm105A can move the end effectors 105B and the cutting guide 105D intoposition to match the location of the femoral or tibial cut to beperformed in accordance with the surgical plan. This can reduce thelikelihood of error, allowing the vision system and a processorutilizing that vision system to implement the surgical plan to place acutting guide 105D at the precise location and orientation relative tothe tibia or femur to align a cutting slot of the cutting guide with thecut to be performed according to the surgical plan. Then, a surgeon canuse any suitable tool, such as an oscillating or rotating saw or drillto perform the cut (or drill a hole) with perfect placement andorientation because the tool is mechanically limited by the features ofthe cutting guide 105D. In some embodiments, the cutting guide 105D mayinclude one or more pin holes that are used by a surgeon to drill andscrew or pin the cutting guide into place before performing a resectionof the patient tissue using the cutting guide. This can free the roboticarm 105A or ensure that the cutting guide 105D is fully affixed withoutmoving relative to the bone to be resected. For example, this procedurecan be used to make the first distal cut of the femur during a totalknee arthroplasty. In some embodiments, where the arthroplasty is a hiparthroplasty, cutting guide 105D can be fixed to the femoral head or theacetabulum for the respective hip arthroplasty resection. It should beunderstood that any arthroplasty that utilizes precise cuts can use therobotic arm 105A and/or cutting guide 105D in this manner.

The Resection Equipment 110 is provided with a variety of commands toperform bone or tissue operations. As with the Effector Platform 105,position information may be provided to the Resection Equipment 110 tospecify where it should be located when performing resection. Othercommands provided to the Resection Equipment 110 may be dependent on thetype of resection equipment. For example, for a mechanical or ultrasonicresection tool, the commands may specify the speed and frequency of thetool. For Radiofrequency Ablation (RFA) and other laser ablation tools,the commands may specify intensity and pulse duration.

Some components of the CASS 100 do not need to be directly controlled bythe Surgical Computer 150; rather, the Surgical Computer 150 only needsto activate the component, which then executes software locallyspecifying the manner in which to collect data and provide it to theSurgical Computer 150. In the example of FIG. 2A, there are twocomponents that are operated in this manner: the Tracking System 115 andthe Tissue Navigation System 120.

The Surgical Computer 150 provides the Display 125 with anyvisualization that is needed by the Surgeon 111 during surgery. Formonitors, the Surgical Computer 150 may provide instructions fordisplaying images, GUIs, etc., using techniques known in the art. Thedisplay 125 can include various aspects of the workflow of a surgicalplan. During the registration process, for example, the display 125 canshow a preoperatively constructed 3D bone model and depict the locationsof the probe as the surgeon uses the probe to collect locations ofanatomical landmarks on the patient. The display 125 can includeinformation about the surgical target area. For example, in connectionwith a TKA, the display 125 can depict the mechanical and anatomicalaxes of the femur and tibia. The display 125 can depict varus and valgusangles for the knee joint based on a surgical plan, and the CASS 100 candepict how such angles will be affected if contemplated revisions to thesurgical plan are made. Accordingly, the display 125 is an interactiveinterface that can dynamically update and display how changes to thesurgical plan would impact the procedure and the final position andorientation of implants installed on bone.

As the workflow progresses to preparation of bone cuts or resections,the display 125 can depict the planned or recommended bone cuts beforeany cuts are performed. The surgeon 111 can manipulate the image displayto provide different anatomical perspectives of the target area and canhave the option to alter or revise the planned bone cuts based onintraoperative evaluation of the patient. The display 125 can depict howthe chosen implants would be installed on the bone if the planned bonecuts are performed. If the surgeon 111 choses to change the previouslyplanned bone cuts, the display 125 can depict how the revised bone cutswould change the position and orientation of the implant when installedon the bone.

The display 125 can provide the surgeon 111 with a variety of data andinformation about the patient, the planned surgical intervention, andthe implants. Various patient-specific information can be displayed,including real-time data concerning the patient's health such as heartrate, blood pressure, etc. The display 125 can also include informationabout the anatomy of the surgical target region including the locationof landmarks, the current state of the anatomy (e.g., whether anyresections have been made, the depth and angles of planned and executedbone cuts), and future states of the anatomy as the surgical planprogresses. The display 125 can also provide or depict additionalinformation about the surgical target region. For a TKA, the display 125can provide information about the gaps (e.g., gap balancing) between thefemur and tibia and how such gaps will change if the planned surgicalplan is carried out. For a TKA, the display 125 can provide additionalrelevant information about the knee joint such as data about the joint'stension (e.g., ligament laxity) and information concerning rotation andalignment of the joint. The display 125 can depict how the plannedimplants' locations and positions will affect the patient as the kneejoint is flexed. The display 125 can depict how the use of differentimplants or the use of different sizes of the same implant will affectthe surgical plan and preview how such implants will be positioned onthe bone. The CASS 100 can provide such information for each of theplanned bone resections in a TKA or THA. In a TKA, the CASS 100 canprovide robotic control for one or more of the planned bone resections.For example, the CASS 100 can provide robotic control only for theinitial distal femur cut, and the surgeon 111 can manually perform otherresections (anterior, posterior and chamfer cuts) using conventionalmeans, such as a 4-in-1 cutting guide or jig 105D.

The display 125 can employ different colors to inform the surgeon of thestatus of the surgical plan. For example, un-resected bone can bedisplayed in a first color, resected bone can be displayed in a secondcolor, and planned resections can be displayed in a third color.Implants can be superimposed onto the bone in the display 125, andimplant colors can change or correspond to different types or sizes ofimplants.

The information and options depicted on the display 125 can varydepending on the type of surgical procedure being performed. Further,the surgeon 111 can request or select a particular surgical workflowdisplay that matches or is consistent with his or her surgical planpreferences. For example, for a surgeon 111 who typically performs thetibial cuts before the femoral cuts in a TKA, the display 125 andassociated workflow can be adapted to take this preference into account.The surgeon 111 can also preselect that certain steps be included ordeleted from the standard surgical workflow display. For example, if asurgeon 111 uses resection measurements to finalize an implant plan butdoes not analyze ligament gap balancing when finalizing the implantplan, the surgical workflow display can be organized into modules, andthe surgeon can select which modules to display and the order in whichthe modules are provided based on the surgeon's preferences or thecircumstances of a particular surgery. Modules directed to ligament andgap balancing, for example, can include pre- and post-resectionligament/gap balancing, and the surgeon 111 can select which modules toinclude in their default surgical plan workflow depending on whetherthey perform such ligament and gap balancing before or after (or both)bone resections are performed.

For more specialized display equipment, such as AR HMDs, the SurgicalComputer 150 may provide images, text, etc., using the data formatsupported by the equipment. For example, if the Display 125 is aholography device such as the Microsoft HoloLens™ or Magic Leap One™,the Surgical Computer 150 may use the HoloLens Application ProgramInterface (API) to send commands specifying the position and content ofholograms displayed in the field of view of the Surgeon 111.

In some embodiments, one or more surgical planning models may beincorporated into the CASS 100 and used in the development of thesurgical plans provided to the surgeon 111. The term “surgical planningmodel” refers to software that simulates the biomechanics performance ofanatomy under various scenarios to determine the optimal way to performcutting and other surgical activities. For example, for knee replacementsurgeries, the surgical planning model can measure parameters forfunctional activities, such as deep knee bends, gait, etc., and selectcut locations on the knee to optimize implant placement. One example ofa surgical planning model is the LIFEMOD™ simulation software from SMITHAND NEPHEW, INC. In some embodiments, the Surgical Computer 150 includescomputing architecture that allows full execution of the surgicalplanning model during surgery (e.g., a GPU-based parallel processingenvironment). In other embodiments, the Surgical Computer 150 may beconnected over a network to a remote computer that allows suchexecution, such as a Surgical Data Server 180 (see FIG. 2C). As analternative to full execution of the surgical planning model, in someembodiments, a set of transfer functions are derived that simplify themathematical operations captured by the model into one or more predictorequations. Then, rather than execute the full simulation during surgery,the predictor equations are used. Further details on the use of transferfunctions are described in PCT/US2019/046995 filed Aug. 19, 2019entitled “Patient Specific Surgical Method and System,” the entirety ofwhich is incorporated herein by reference.

FIG. 2B shows examples of some of the types of data that can be providedto the Surgical Computer 150 from the various components of the CASS100. In some embodiments, the components may stream data to the SurgicalComputer 150 in real-time or near real-time during surgery. In otherembodiments, the components may queue data and send it to the SurgicalComputer 150 at set intervals (e.g., every second). Data may becommunicated using any format known in the art. Thus, in someembodiments, the components all transmit data to the Surgical Computer150 in a common format. In other embodiments, each component may use adifferent data format, and the Surgical Computer 150 is configured withone or more software applications that enable translation of the data.

In general, the Surgical Computer 150 may serve as the central pointwhere CASS data is collected. The exact content of the data will varydepending on the source. For example, each component of the EffectorPlatform 105 provides a measured position to the Surgical Computer 150.Thus, by comparing the measured position to a position originallyspecified by the Surgical Computer 150, the Surgical Computer canidentify deviations that take place during surgery.

The Resection Equipment 110 can send various types of data to theSurgical Computer 150 depending on the type of equipment used. Exampledata types that may be sent include the measured torque, audiosignatures, and measured displacement values. Similarly, the TrackingTechnology 115 can provide different types of data depending on thetracking methodology employed. Example tracking data types includeposition values for tracked items (e.g., anatomy, tools, etc.),ultrasound images, and surface or landmark collection points or axes.The Tissue Navigation System 120 provides the Surgical Computer 150 withanatomic locations, shapes, etc., as the system operates.

Although the Display 125 generally is used for outputting data forpresentation to the user, it may also provide data to the SurgicalComputer 150. For example, for embodiments where a monitor is used aspart of the Display 125, the Surgeon 111 may interact with a GUI toprovide inputs which are sent to the Surgical Computer 150 for furtherprocessing. For AR applications, the measured position and displacementof the HMD may be sent to the Surgical Computer 150 so that it canupdate the presented view as needed.

During the post-operative phase of the episode of care, various types ofdata can be collected to quantify the overall improvement ordeterioration in the patient's condition as a result of the surgery. Thedata can take the form of, for example, self-reported informationreported by patients via questionnaires. For example, in the context ofa knee replacement surgery, functional status can be measured with anOxford Knee Score questionnaire, and the post-operative quality of lifecan be measured with a EQSD-5L questionnaire. Other examples in thecontext of a hip replacement surgery may include the Oxford Hip Score,Harris Hip Score, and WOMAC (Western Ontario and McMaster UniversitiesOsteoarthritis index). Such questionnaires can be administered, forexample, by a healthcare professional directly in a clinical setting orusing a mobile app that allows the patient to respond to questionsdirectly. In some embodiments, the patient may be outfitted with one ormore wearable devices that collect data relevant to the surgery. Forexample, following a knee surgery, the patient may be outfitted with aknee brace that includes sensors that monitor knee positioning,flexibility, etc. This information can be collected and transferred tothe patient's mobile device for review by the surgeon to evaluate theoutcome of the surgery and address any issues. In some embodiments, oneor more cameras can capture and record the motion of a patient's bodysegments during specified activities postoperatively. This motioncapture can be compared to a biomechanics model to better understand thefunctionality of the patient's joints and better predict progress inrecovery and identify any possible revisions that may be needed.

The post-operative stage of the episode of care can continue over theentire life of a patient. For example, in some embodiments, the SurgicalComputer 150 or other components comprising the CASS 100 can continue toreceive and collect data relevant to a surgical procedure after theprocedure has been performed. This data may include, for example,images, answers to questions, “normal” patient data (e.g., blood type,blood pressure, conditions, medications, etc.), biometric data (e.g.,gait, etc.), and objective and subjective data about specific issues(e.g., knee or hip joint pain). This data may be explicitly provided tothe Surgical Computer 150 or other CASS component by the patient or thepatient's physician(s). Alternatively or additionally, the SurgicalComputer 150 or other CASS component can monitor the patient's EMR andretrieve relevant information as it becomes available. This longitudinalview of the patient's recovery allows the Surgical Computer 150 or otherCASS component to provide a more objective analysis of the patient'soutcome to measure and track success or lack of success for a givenprocedure. For example, a condition experienced by a patient long afterthe surgical procedure can be linked back to the surgery through aregression analysis of various data items collected during the episodeof care. This analysis can be further enhanced by performing theanalysis on groups of patients that had similar procedures and/or havesimilar anatomies.

In some embodiments, data is collected at a central location to providefor easier analysis and use. Data can be manually collected from variousCASS components in some instances. For example, a portable storagedevice (e.g., USB stick) can be attached to the Surgical Computer 150into order to retrieve data collected during surgery. The data can thenbe transferred, for example, via a desktop computer to the centralizedstorage. Alternatively, in some embodiments, the Surgical Computer 150is connected directly to the centralized storage via a Network 175 asshown in FIG. 2C.

FIG. 2C illustrates a “cloud-based” implementation in which the SurgicalComputer 150 is connected to a Surgical Data Server 180 via a Network175. This Network 175 may be, for example, a private intranet or theInternet. In addition to the data from the Surgical Computer 150, othersources can transfer relevant data to the Surgical Data Server 180. Theexample of FIG. 2C shows 3 additional data sources: the Patient 160,Healthcare Professional(s) 165, and an EMR Database 170. Thus, thePatient 160 can send pre-operative and post-operative data to theSurgical Data Server 180, for example, using a mobile app. TheHealthcare Professional(s) 165 includes the surgeon and his or her staffas well as any other professionals working with Patient 160 (e.g., apersonal physician, a rehabilitation specialist, etc.). It should alsobe noted that the EMR Database 170 may be used for both pre-operativeand post-operative data. For example, assuming that the Patient 160 hasgiven adequate permissions, the Surgical Data Server 180 may collect theEMR of the Patient pre-surgery. Then, the Surgical Data Server 180 maycontinue to monitor the EMR for any updates post-surgery.

At the Surgical Data Server 180, an Episode of Care Database 185 is usedto store the various data collected over a patient's episode of care.The Episode of Care Database 185 may be implemented using any techniqueknown in the art. For example, in some embodiments, an SQL-baseddatabase may be used where all of the various data items are structuredin a manner that allows them to be readily incorporated in two SQL'scollection of rows and columns. However, in other embodiments a No-SQLdatabase may be employed to allow for unstructured data, while providingthe ability to rapidly process and respond to queries. As is understoodin the art, the term “No-SQL” is used to define a class of data storesthat are non-relational in their design. Various types of No-SQLdatabases may generally be grouped according to their underlying datamodel. These groupings may include databases that use column-based datamodels (e.g., Cassandra), document-based data models (e.g., MongoDB),key-value based data models (e.g., Redis), and/or graph-based datamodels (e.g., Allego). Any type of No-SQL database may be used toimplement the various embodiments described herein and, in someembodiments, the different types of databases may support the Episode ofCare Database 185.

Data can be transferred between the various data sources and theSurgical Data Server 180 using any data format and transfer techniqueknown in the art. It should be noted that the architecture shown in FIG.2C allows transmission from the data source to the Surgical Data Server180, as well as retrieval of data from the Surgical Data Server 180 bythe data sources. For example, as explained in detail below, in someembodiments, the Surgical Computer 150 may use data from past surgeries,machine learning models, etc., to help guide the surgical procedure.

In some embodiments, the Surgical Computer 150 or the Surgical DataServer 180 may execute a de-identification process to ensure that datastored in the Episode of Care Database 185 meets Health InsurancePortability and Accountability Act (HIPAA) standards or otherrequirements mandated by law. HIPAA provides a list of certainidentifiers that must be removed from data during de-identification. Theaforementioned de-identification process can scan for these identifiersin data that is transferred to the Episode of Care Database 185 forstorage. For example, in one embodiment, the Surgical Computer 150executes the de-identification process just prior to initiating transferof a particular data item or set of data items to the Surgical DataServer 180. In some embodiments, a unique identifier is assigned to datafrom a particular episode of care to allow for re-identification of thedata if necessary.

Although FIGS. 2A-2C discuss data collection in the context of a singleepisode of care, it should be understood that the general concept can beextended to data collection from multiple episodes of care. For example,surgical data may be collected over an entire episode of care each timea surgery is performed with the CASS 100 and stored at the SurgicalComputer 150 or at the Surgical Data Server 180. As explained in furtherdetail below, a robust database of episode of care data allows thegeneration of optimized values, measurements, distances, or otherparameters and other recommendations related to the surgical procedure.In some embodiments, the various datasets are indexed in the database orother storage medium in a manner that allows for rapid retrieval ofrelevant information during the surgical procedure. For example, in oneembodiment, a patient-centric set of indices may be used so that datapertaining to a particular patient or a set of patients similar to aparticular patient can be readily extracted. This concept can besimilarly applied to surgeons, implant characteristics, CASS componentversions, etc.

Further details of the management of episode of care data is describedin PCT/US2019/067845, filed Dec. 20, 2019 and entitled “Methods andSystems for Providing an Episode of Care,” the entirety of which isincorporated herein by reference.

Open Versus Closed Digital Ecosystems

In some embodiments, the CASS is designed to operate as a self-containedor “closed” digital ecosystem. Each component of the CASS isspecifically designed to be used in the closed ecosystem and data isgenerally not accessible to devices outside of the digital ecosystem.For example, in some embodiments, each component includes software orfirmware that implements proprietary protocols for activities such ascommunication, storage, security, etc. The concept of a closed digitalecosystem may be desirable for a company that wants to control allcomponents of the CASS to ensure that certain compatibility, security,and reliability standards are met. For example, the CASS can be designedsuch that a new component cannot be used with the CASS unless it iscertified by the company.

In other embodiments, the CASS is designed to operate as an “open”digital ecosystem. In these embodiments, the components may be producedfrom a variety of different companies, and components implementstandards for activities, such as communication, storage, and security.Thus, by using these standards, any company can freely build anindependent, compliant component of the CASS platform. Data may betransferred between components using publicly available applicationprogramming interfaces (APIs) and open, shareable data formats.

CASS Queries and CASS Recommendations

Simple joints, such as the ball and socket joint (e.g., hip andshoulder) or the pivot joint (e.g., elbow), or more complex joints, suchas the condylar joint (e.g., knee joint), are incredibly intricatesystems whose performance can be significantly affected by variousfactors. Procedures for replacing, resurfacing, or otherwise repairingthese joints are common, such as in response to damage or otherdegradation of the joint. For instance, TKA, which replaces thearticular surfaces of the femur, tibia and/or patella with artificialimplants, is a common procedure for patients suffering from degradationor trauma to the knee joint.

Selecting the optimal parameters for performing joint surgery ischallenging. To continue with the example of knee replacement surgery, asurgeon can place a first prosthesis on the distal end of the femur anda second prosthesis at the proximal end of the tibia, or the surgeon caninstall the prostheses in the opposite order. The surgeon seeks tooptimally place the prostheses with respect to various parameters, suchas the gap between the prostheses throughout a range of motion.Misplacement of an implant could have a negative impact on the patient'squality of life post-surgery. For example, if the gap between the tibiaand the femur is too small at any time during the range of motion, thepatient can experience painful binding. On the other hand, if the gap istoo large, the knee joint is too loose and can become unstable.

In some embodiments, the CASS (or preoperative planning application) 100is configured to generate recommendations based on queries received fromthe surgeon or the surgical staff. Examples of recommendations that maybe provided by the CASS 100 include, without limitation, optimization ofone or more surgical parameters, optimization of implant position andorientation relative to a reference point or points, such as ananatomical or mechanical axis, a modification of the surgical plan, or adescription of how to achieve a particular result. As noted above, thevarious components of the CASS 100 generate various types of data thatcollectively define the state of the system. Additionally, the CASS 100may have access to various types of pre-operative data (e.g., patientdemographics, pre-operative images, etc.), historical data (e.g., fromother surgeries performed by the same or a different surgeon), andsimulation results. Based on all of this data, the CASS 100 can operatein a dynamic manner and allow the surgeon to intelligently modify thesurgical plan on-the-fly as needed. In some embodiments, thesemodifications are performed before surgery (e.g., before printingcutting guides). In some embodiments, where custom cutting guides arenot used (e.g., a selection of non-patient specific cutting guides areavailable that can be selected and placed by a CASS), modifications canbe made during surgery by the CASS. Thus, for example, in someembodiments, the CASS 100 notifies the surgeon via a display 125 of amodified surgical plan or an optimization based on a condition that wasnot detected pre-operatively.

In some embodiments, a surgical plan can be created prior to surgeryusing preoperative images and data. These images can include x-ray, CT,MRI, and ultrasound images. Data can include characteristics of thepatient including joint geometry, age, weight, activity level, and thelike, and data about the prosthetic to be implanted. This plan can thenbe modified based on additional information gathered intraoperatively.For example, additional medical images can be taken during the surgicalprocedure and may be used to modify the surgical plan based onadditional physiological details gleaned from such images. In someembodiments, the surgical plan is based on patient information, withoutthe need to capture three-dimensional images, such as via a CT or Millscan of the patient. Additional optical or x-ray images can be takenduring the procedure to provide additional detail and alter the surgicalplan, allowing the surgical plan to be developed and modified withoutthe need for expensive medical imaging preoperatively.

The processor of the CASS 100 can recommend any aspect of the surgicalplan and modify this recommendation based on new data collected duringsurgery. For example, the processor of the CASS 100 can optimizeanteversion and abduction angles for hip cup placement (in hiparthroplasty) or the depth and orientation of the distal and posteriorfemoral cut planes and patella configuration (in PKA/TKA), in responseto images captured before or during surgery. Once an initial defaultplan is generated, a surgeon can ask for a recommendation on aparticular aspect of the surgery and may deviate from the initialsurgical plan. Requests for a recommendation can result in a new plan,partial deviation from the initial or default plan, or confirmation andapproval of the initial plan. Accordingly, by using a data-drivenapproach using a processor of the CASS 100, the surgical plan can beupdated and optimized as the procedure transpires. These optimizationsand recommendations, as explained throughout, can be generated by aprocessor before or during a procedure based on a statistical model ofpatient anatomy from a plurality of simulations or a transfer functionthat is informed by the simulations and specific details of the patientanatomy being operated upon. Accordingly, any additional data collectedabout patient anatomy can be used to update the statistical model forthat patient to optimize implant characteristics to maximize performancecriteria of the expected outcome of the procedure from the surgicalplan.

FIG. 3A provides a high-level overview of how recommendations can begenerated. This workflow begins at 305 with the surgical staff executingthe surgical plan. This surgical plan could be the original plangenerated based on pre-operative or intra-operative imaging and data, orthe plan could be a modification of the original surgical plan. At 310,the surgeon or a member of the surgical staff requests a recommendationof how to address one or more issues in the surgical procedure. Forexample, the surgeon may request a recommendation for how to optimallyalign an implant based on intra-operative data (e.g., acquired using apoint probe or new images). In some embodiments, the request may be madeby manually entering a specific request into a GUI or voice interface ofthe CASS 100. In other embodiments, the CASS 100 includes one or moremicrophones that collect verbal requests or queries from the surgeonthat are translated into formal requests (e.g., using natural languageprocessing techniques).

Continuing with reference to FIG. 3A, at 315 the recommendation isprovided to the surgeon. Various techniques can be used to provide therecommendation. For example, where the recommendation provides arecommended cut to be made or a recommended implant orientationalignment, a graphical representation of the recommendation may bedepicted on a display 125 of the CASS 100. In one embodiment, therecommendation may be overlaid on the patient's anatomy in an AR HMD155. The resulting performance characteristics (such as medial andlateral condylar gaps and patellar groove tracking for various flexionsand curves showing ligament tension for a range of motion, for a PKA/TKAor plots of range of motion and center of pressure and edge loadingstresses between femoral head and acetabular for THA) can be presented.In some embodiments, the information can be conveyed via a display 125(which could include an HMD 155) to a user in the form of a plot,number, or by changing a color or an indicator. For example, in aPKA/TKA an image of the patella (overlaid on a patient image, forexample) could glow red or flash when a change to the plan or thepatient data indicates via the statistical model that the patella willencounter tracking problems relative to the patella groove or overstrainpatellar ligaments without additional changes. For a THA, a portion ofthe acetabular cup can glow to indicate where there is an increased edgeloading or dislocation risk. In some embodiments, the interface can theninvite the user to click for a recommended solution, such as patellarstuffing, ligament release, or changes to the pose of a patellar implantor the femoral implant (PKA/TKA) or acetabulare cup anteversion andabduction angles (THA) to optimize performance.

Aside from the recommendation, in some embodiments, the CASS/planningsystem 100 may also provide a rationale for the recommendation. Forexample, for a recommended alignment or orientation of an implant, theCASS 100 may provide a listing of patient-specific features oractivities that influenced the recommendation. The CASS-recommendedalignment or orientation of an implant may further refer to a referenceframe or point, such as an anatomical or mechanical axis or a distancefrom a bone or bone landmark. Additionally, as shown in 320, the CASSmay model how selecting a particular recommendation will impact the restof the surgical procedure. For example, prior to surgery, a defaultcutting guide may be generated based on preoperative 3-dimensional CT orMM scans. During surgery, the surgeon may acquire high-resolution dataof the patient's anatomy from an Mill or using a point probe or opticalcamera once an incision is made. The CASS 100 may use such data tocreate a new or updated plan or recommendation regarding the resectionof bone tissue using a resection tool or cutting guide. At step 320, theimpact of this revised plan or recommendation may be presented in theform of revised alignment instructions, etc. The surgeon may also bepresented with a plurality of recommendations and view the impact ofeach on the surgical plan. For example, two viable recommended boneresection plans or recommendations could be generated, and the surgeoncan decide which one to execute based on the impact each recommendationhas on the subsequent steps of the surgery. The surgeon may also bepresented with animations of range of motion or dynamic activities (suchas walking or ascending stairs etc.) that are a functional consequenceof each recommendation to allow the surgeon to better understand how therecommendation will affect patient motion characteristics. In someembodiments, the surgeon has the ability to select individual data itemsor parameters (e.g., alignment, tension and flexion gaps, etc.) foroptimization recommendations. Finally, once the surgeon selects aparticular recommendation, it is executed at step 325. For example, inembodiments where a custom cutting guide is manufactured prior tosurgery, step 325 can be executed by printing and delivering the cuttingguide to the surgeon for use during surgery. In embodiments where arobot arm holds a cutting guide at a specific predetermined location,the commands to place the cutting guide can be sent to the robot arm aspart of the CASS workflow. In embodiments that do not use a cuttingguide, the CASS can receive instructions to assist the surgeon andresecting the femoral components and tibia in accordance with therecommendation.

The CASS 100 can present recommendations to the surgeon or the surgicalstaff preoperatively or at any time during surgery. In some instances,the surgeon may expressly request the recommendation as discussed abovewith respect to FIG. 3A. In other embodiments, the CASS 100 may beconfigured to execute recommendation algorithms as a background processwhile the surgery is proceeding based on the available data. When a newrecommendation is generated, the CASS 100 may notify the surgeon withone or more notification mechanisms. For example, a visual indicator maybe presented on a display 125 of the CASS 100. Ideally, a notificationmechanism should be relatively unobtrusive such that it does notinterfere with surgery. For example, in one embodiment, as the surgeonnavigates through the surgical plan, different text colors could be usedto indicate that a recommendation is available. In some embodiments,where an AR or VR headset 155 is used, the recommendation may beprovided into the user's visual field and highlighted to draw the user'sattention to either the recommendation or the fact that a recommendationis available. The user (e.g., a surgeon or technician) can then interactwith the recommendation or solicitation for recommendation in the AR orVR user interface using any of the means described below. In theseembodiments, the CASS 100 can treat user headsets 155 as additionaldisplays and communicate information to be displayed thereon using anyconventional means used to communicate with displays.

To illustrate one type of recommendation that may be performed with theCASS 100, a technique for optimizing surgical parameters is disclosedbelow. The term “optimization” in this context means selection ofparameters that are optimal based on certain specified criteria. In anextreme case, optimization can refer to selecting optimal parameter(s)based on data from the entire episode of care, including anypre-operative data, the state of CASS data at a given point in time, andpost-operative goals. Moreover, optimization may be performed usinghistorical data, such as data generated during past surgeries involving,for example, the same surgeon, past patients with physicalcharacteristics similar to the current patient, or the like.

The optimized parameters may depend on the portion of the patient'sanatomy to be operated on. For example, for knee surgeries, the surgicalparameters may include positioning information for the femoral andtibial components including, without limitation, rotational alignment(e.g., varus/valgus rotation, external rotation, flexion rotation forthe femoral component, posterior slope of the tibial component),resection depths (e.g., varus knee, valgus knee), and implant type, sizeand position. The positioning information may further include surgicalparameters for the combined implant, such as overall limb alignment,combined tibiofemoral hyperextension, and combined tibiofemoralresection. Additional examples of parameters that could be optimized fora given TKA femoral implant by the CASS 100 include the following:

Exemplary Parameter Reference Recommendation (s) Size Posterior Thelargest sized implant that does not overhang medial/lateral bone edgesor overhang the anterior femur. A size that does not result inoverstuffing the patella femoral joint Implant Position- Medial/lateralcortical Center the implant Medial Lateral bone edges evenly between themedial/lateral cortical bone edges Resection Depth- Distal and posterior6 mm of bone Varus Knee lateral Resection Depth- Distal and posterior 7mm of bone Valgus Knee medial Rotation- Mechanical Axis 1° varusVarus/Valgus Rotation-External Transepicondylar 1° external from theAxis transepicondylar axis Rotation-Flexion Mechanical Axis 3° flexed

Additional examples of parameters that could be optimized for a givenTKA tibial implant by the CASS include the following:

Exemplary Parameter Reference Recommendation (s) Size Posterior Thelargest sized implant that does not overhang the medial, lateral,anterior, and posterior tibial edges Implant Position Medial/lateral andCenter the implant anterior/posterior evenly between the cortical boneedges medial/lateral and anterior/posterior cortical bone edgesResection Depth- Lateral/Medial 4 mm of bone Varus Knee Resection Depth-Lateral/Medial 5 mm of bone Valgus Knee Rotation- Mechanical Axis 1°valgus Varus/Valgus Rotation-External Tibial Anterior 1° external fromthe Posterior Axis tibial anterior paxis Posterior Slope Mechanical Axis3° posterior slope

For hip surgeries, the surgical parameters may comprise femoral neckresection location and angle, cup inclination angle, cup anteversionangle, cup depth, femoral stem design, femoral stem size, fit of thefemoral stem within the canal, femoral offset, leg length, and femoralversion of the implant.

Shoulder parameters may include, without limitation, humeral resectiondepth/angle, humeral stem version, humeral offset, glenoid version andinclination, as well as reverse shoulder parameters such as humeralresection depth/angle, humeral stem version, Glenoid tilt/version,glenosphere orientation, glenosphere offset and offset direction.

Various conventional techniques exist for optimizing surgicalparameters. However, these techniques are typically computationallyintensive and, thus, parameters often need to be determinedpre-operatively. As a result, the surgeon is limited in his or herability to make modifications to optimized parameters based on issuesthat may arise during surgery. Moreover, conventional optimizationtechniques typically operate in a “black box” manner with little or noexplanation regarding recommended parameter values. Thus, if the surgeondecides to deviate from a recommended parameter value, the surgeontypically does so without a full understanding of the effect of thatdeviation on the rest of the surgical workflow, or the impact of thedeviation on the patient's post-surgery quality of life.

To address these and other drawbacks of conventional optimizationtechnology, in some embodiments, optimization may be performed duringthe surgical workflow using a button or other component in the GUIspresented to the surgeon (e.g., on the Display 125 or the AR HMD 155).For the purposes of the following discussion, a surgeon or otherhealthcare professional may invoke a request for a recommendation orinput from the CASS 100 using any means such as an oral request/commandor a manual input (e.g., using a touch screen or button). For purposesof this application, these types of queries or requests for arecommended course of action, a recommended parameter optimization, orother feedback to be provided to the surgeon or medical professional inresponse to such query or request are referred to as a CASSRecommendation Request or “CASSRR.” A CASS Recommendation Request may beinvoked or activated by the surgeon or healthcare professional at anytime during surgery. For example, for a TKA, a CASSRR may be invokedduring the femoral implant planning stage, the tibial implant planningstage, and/or the gap planning stage. In a THA surgery, a CASSRR may beused during femoral neck resection, acetabular implant placement,femoral implant placement, and implant selection (e.g., size, offset,bearing type, etc.). A CASSRR may be invoked, for example, by pushingthe button or by speaking a particular command (e.g., “optimize gap”).As noted above, the recommendation system can be configured to offer orprompt the surgeon for a recommendation or optimization at any timeduring surgery.

FIGS. 3B-3E show examples of GUIs that may be used during the surgicalworkflow using a CASS/planning app, such as the one depicted in FIG. 1.These GUIs may be displayed, for example, on the Display 125 of the CASS100 or on a workstation during the planning stages for an upcomingsurgery. In each example, an AI-driven recommendation request may beinvoked using a button that is presented as a visual component of theinterface. Specifically, FIG. 3B shows an example Implant PlacementInterface 330 where a recommendation button 335 (labeled as a CASSRR inthis example) is shown in the lower left hand corner. Similarly, FIG. 3Cshows an example Gap Planning Interface 340 with the Button 335.

Invoking a CASSRR may cause an Optimization Parameterization Interface370 to be displayed as shown in FIG. 3D when a user requests arecommendation. In the example of FIG. 3D, a CASSRR/recommendationrequest is invoked using a button that has been activated on the ImplantPlacement Interface 330 (shown in FIG. 3B). The OptimizationParameterization Interface 370 includes Degree of Freedom (DoF) Buttons345 related to movement of the implant (e.g., translation, rotation,etc.). Activation of any of the DoF Buttons 345 “locks” a correspondingdegree of freedom during the optimization analysis. For example, if thesurgeon is satisfied with the anterior or posterior positioning, thesurgeon may activate the “A” Button or the “P” Button to lock theanterior position or the posterior position, respectively. In someembodiments, the locked buttons change color or provide a different typeof visual indicator when toggled into the locked position. In theexample of FIG. 3D, the “S” button (corresponding to superiorpositioning) has been locked as shown by the graphical depiction of alock to the right of the button. It should be noted that the use ofbuttons for locking positions is but one example of how the surgeon caninterface with the CASS 100; in other embodiments, for example, thesurgeon may verbally request locking of a particular position (e.g.,“lock superior positioning”) or, in the VR context, gestures may beemployed. The values that can be fixed may depend on availableoptimization factors and the surgeon's comfort level with optimizingdifferent aspects of the surgery. For example, it has been assumed thusfar that the optimization is performed from a wholly functionalkinematic perspective. Thus, the optimizing system does not trulyunderstand how the implant interacts with bone. This may be addressed,for example, by adding an image-based analysis to the optimization.However, for embodiments where such analysis is not performed, thesurgeon may want to constrain matters based on how he/she views the bonefit. For example, regarding the femoral component of the implant in aTKA surgery, when the optimization is performed, the anterior overhangmay not be known. That would imply that the surgeon may want to adjustthe A-P position of the femoral component because he or she is viewingthe implant in terms of bone fit and not as a function of a kinematicperformance. Thus, the surgeon may determine the A-P position, therotation, and possibly the joint line used in determining the finalimplant position, orientation and location. In this way, the surgeon cansupplement the knowledge provided from a computer-generated optimizationrun allowing the surgical plan that is implemented by surgeon to deviatefrom a computational recommendation.

Buttons may also be used to provide boundary controls for a givenparameter used for optimization. In the example of FIG. 3D, there aretwo Boundary Control Buttons 355 for the posterior slope. These can beused to set minimum or maximum values for the relevant parameters thatwill bound the parameters in an optimization. If the right BoundaryControl Button 355 is locked, the optimization can be configured toproduce a value above the current value specified for the posteriorslope. Conversely, if only the left Boundary Control Button 355 islocked, the optimization can be configured to produce values below thecurrent value specified for posterior slope. If both Boundary ControlButtons 355 are locked, then the optimization is configured such that itdoes not change the specified posterior slope value. On the other hand,if neither Boundary Control Buttons 355 is locked, then optimization isfree to change values (within device specifications).

The Optimization Parameterization Interface 370 includes an OptimizationButton 350 that, when activated, causes the implant placement parametersto be optimized using any of the data-drive/AI approaches describedherein. It should be noted that this general concept is not limited toimplant placement; rather, in general, any surgical parameter, or groupof parameters, can be optimized using similar interfaces and techniques.This optimization process is further detailed below. Followingoptimization, the surgeon may be returned to the Implant PlacementInterface 330 (as shown in FIG. 3B) to continue the surgery with theoptimal parameters.

A Toggle Button 365 allows the surgeon to toggle between any two viewsor aspects of the surgical procedure or surgical procedure plan. Forexample, the Toggle Button 365 can provide the surgeon with the currentbone condition and a future bone condition based on the partial orcomplete execution of the surgical plan or the current planned implantposition and an alternative (e.g., recommended) implant position. TheToggle Button 365 could also provide the surgeon with alternative futureconditions of the bone and/or implant depending on whether the surgeonelects to take one course of action as opposed to an alternative courseof action. Activation of this button causes the various images and datapresented on the Optimization Parameterization Interface 370 to beupdated with current or previous alignment information. Thus, thesurgeon can quickly view the impact of any changes. For example, thetoggle feature may allow the surgeon to visualize the prosthesispositioning changes that are suggested by the optimizer relative totheir previous notion of proper implant placement. In one embodiment,during initial use of the system, the user may choose to plan the casewithout optimization, and wish to visualize the impact of automation.Similarly, the user may wish to visualize the impact of ‘locking’various aspects of planning.

If the surgeon wishes to understand the rationale behind theoptimization, the Response and Rationale Button 360 on the OptimizationParameterization Interface 370 may be activated to display the Responseand Rationale Interface 375 shown in FIG. 3E. This interface 375includes an Animation Screen 380 that provides an animation of theanatomy of interest during the performance measurement activity (e.g.,deep knee bend). This animation may be provided as an output of theanatomical modeling software performing the optimization (e.g.,LIFEMOD™) or, alternatively, separate software may be used to generatethe animation based on the output of the optimization software. Theanimation may be depicted, for example, using an animated GIF file or asmall video file. In embodiments where an AR HMD 155 is used, a hologramof the animation can be provided over the relevant anatomy to providefurther contextualization of the simulated behavior.

The Response and Rationale Interface 375 also includes a Response Screen385 that displays plots for various performance or condition measures(e.g., measure v flexion angle). A set of Performance Measure SelectionButtons 390 on the right-hand side of the Response and RationaleInterface 375 allows the surgeon to select various relevant performancemeasures and update the plot shown in the Response Screen 385. In theexample of FIG. 3E, these performance measures include internal-external(IE) rotation, medial and lateral rollback, MCL and LCL strain,iliotibial band (ITB) strain, bone stress, varus-valgus (V-V) rotation,medial-lateral (ML) patella shear, quayd force, and bone interfaceforces. The example shown in screen 385 depicts the lateral and medialgaps throughout the range of flexion, which is a traditional estimate ofTKA performance. Traditionally, the lateral and medial gaps have onlybeen considered at two degrees of flexion.

In order to support the various interfaces described above, thealgorithms supporting a CASSRR/recommendation button should preferablybe performed as quickly as possible to ensure that the surgical workflowis not disrupted. However, the calculations involved with performingoptimization can be computationally intensive. Thus, to simplify therequired processing, a set of predictor equations can be generated basedon a training dataset and simulated performance measurements in someembodiments. These predictor equations provide a simplified form of theparameter space that can be optimized in near real-time. These processescan be executed locally or on a remote server, e.g., in the cloud.

FIG. 4 provides a system diagram illustrating how optimization ofsurgical parameters can be performed, according to some embodiments.This optimization can be performed during a preoperative planning stage,such as in embodiments where a custom cutting guide is created beforesurgery, or intraoperatively, such as in embodiments where a CASS canadjust the exact pose of resection planes robotically or through otherpractical means, such as haptic feedback. Briefly, the Surgeon 111provides certain patient-specific parameters and parameters related tothe implant to the Surgical Computer 150 (via a GUI presented on theDisplay 125). The Surgeon 111 requests that optimization be performed.The Surgical Computer 150 uses the parameters to retrieve a set ofpredictor equations from an Equation Database 410 stored on the SurgicalData Server 180. In embodiments where a cloud-based architecture is notused, this Equation Database 410 may be stored directly on the SurgicalComputer 150. Optimization of the equation set provides the desiredoptimization (e.g., optimal implant alignment and positioning). Thisinformation can then be presented to the Surgeon 111 via the Display 125of the CASS 100 (see, e.g., FIGS. 3D and 3E).

As explained in greater detail below, each equation dataset provideskinematic and kinetic responses for a group of parameters. In someembodiments, the Equation Database 410 is populated using equationdatasets derived by a Simulation Computer 405 based on a set of trainingdata. The training dataset comprises surgical datasets previouslycollected by the CASS 100 or another surgical system. Each surgicaldataset may include, for example, information on the patient's geometry,how the implant was positioned and aligned during surgery, ligamenttensions, etc. Data may be gathered using any technique known in theart. For example, for ligament tension, a robotic assisted technique maybe employed as described in PCT/US2019/067848, filed Dec. 20, 2019,entitled “Actuated Retractor with Tension Feedback,” the entirety ofwhich is incorporated herein by reference. An additional example isprovided by PCT/US2019/045551 and PCT/US2019/045564, filed Aug. 7, 2019and entitled “Force-Indicating Retractor Device and Methods of Use,” theentirety of which is incorporated herein by reference.

For each surgical dataset, a Simulation Computer 405 executes ananatomical simulation on the surgical dataset to determine a set ofkinematic and kinetic responses. Non-limiting examples of suitableanatomical modeling tools that may be used include LIFEMOD™ or KNEESIM™(both available from LIFEMODELER, INC. of San Clemente, Calif., asubsidiary of SMITH AND NEPHEW, INC.). Additional examples for usingbiomechanical modeling during surgery are described in U.S. Pat. No.8,794,977 entitled “Implant Training System”; U.S. Pat. No. 8,712,933entitled “Systems and methods for determining muscle force throughdynamic gain optimization of a muscle PID controller for designing areplacement prosthetic joint”; and U.S. Pat. No. 8,412,669 entitled“Systems and methods for determining muscle force through dynamic gainoptimization of a muscle PID controller”; the entire contents of whichare incorporated herein by reference.

In addition to determining the responses for the surgical dataset, theSimulation Computer 405 may also be used to supplement the real-worldsurgical datasets with artificially generated surgical datasets thatfill in any gaps in the training dataset. For example, in oneembodiment, Monte Carlo analysis is performed using small permutationsof various factors in the training set to see how they affect theresponse. Thus, a relatively small set of real-world surgical data(e.g., 1,000 datasets) can be essentially extrapolated to produce anexponentially larger dataset covering various patient anatomies, implantgeometries, etc. Once the dataset has been populated, one or moreequation fitting techniques generally known in the art may be used toderive the equation datasets stored in the Equation Database 410.

In order to determine the kinematic and kinetic responses used in eachequation dataset, the simulation executed by the Simulation Computer 405may model and simulate various activities that stress the anatomy ofinterest. For example, in the context of TKA or other knee surgeries, aweighted deep knee bend may be used. During a deep knee bend, the kneeflexes down at various angles (e.g., 120°, 130°, etc.) under a certainload and returns to an erect position. During the deep knee bend, loadsoccur on the extensors of the leg (i.e., quadriceps), the flexors of theleg (i.e., hamstrings), the passive ligaments in the knee, etc. As such,the deep knee bend stresses the anterior cruciate ligament (ACL),posterior cruciate ligament (PCL), lateral collateral ligament (LCL) andmedial collateral ligament (MCL). Additionally, the deep knee bendallows the measurement of various kinematics (e.g., how the patella ismoving in relation to the femoral component, how the femur is movingwith respect to the tibia, etc.). It should be noted that this is oneexample of a performance measurement that may be applied and variousother measurements may be used as a supplement or alternative to thedeep knee bend. The knee kinematics can further be simulated using themodel of the knee to perform approximately real-world motions associatedwith dynamic activities, such as walking up or down stairs or swinging agolf club. Other joints can be simulated in the body as simple idealcomponents while the individual ligaments of concern and implantcomponents can be simulated in detail performing the motion underexemplary loads associated with each activity being considered.

FIGS. 5A-5F describe an exemplary joint predictor equation that may beused in an equation dataset in some embodiments. While these figureswill be described with respect to knee arthroplasty, these conceptsapply equally to other arthroplasties, such as hip. Regardless of thearthroplasty being performed, the basic categories can be the same, withthe specific data in each category relevant to the surgery beingperformed. FIG. 5A provides an overview of the knee predictor equation.As will be explained below, the terms of this equation are simplified toallow for understanding of the various terms. Thus, it should beunderstood that the exact mathematical constructs may differ from thoseshown in the figures.

In these predictor equations, the terms on the left-hand side arereferred to as “factors,” while the terms on the right-hand side arereferred to as “responses.” The responses and factors may be associatedwith specific numerical values, although, in at least some embodiments,at least some may be represented as a probability distribution (such asa bell curve) or in another manner reflecting uncertainty about theactual value of the factor or response. As such, the equations mayaccount for uncertainty in certain aspects of this process. Forinstance, in at least some embodiments, it may be difficult to identifysoft tissue attachment locations with certainty, and, accordingly,uncertainty information may be used reflecting a probabilitydistribution of where such soft tissue attachment locations are actuallylocated based on estimated locations identified during image processing.Similarly, in at least some embodiments, rather than determining anexact optimal position and orientation for the orthopedic implant, itmay be desirable to determine optimal position and orientation in thecontext of potential for variability in where the implant will actuallybe positioned and oriented (e.g., to account for tolerances inmanufacturing custom cutting guide instrumentation, variability insurgeons' surgical techniques, etc.).

FIG. 5B illustrates the patient-specific parameters used in the kneepredictor equation. These parameters may be measured by the surgicalstaff based on pre-operative or intra-operative data. As shown in theexample of FIG. 5B, an X-Ray measurement tool may be used to measurevarious anatomical features in images. Examples of patient-specificparameters that may be utilized include load-bearing access (LBA),pelvis width, femoral ML width, tibial ML width, femur length, etc.

FIG. 5C shows the soft tissue balance parameters that are included inthe knee predictor equations. The soft tissue balance parameters may bederived from multiple sources. For example, by default, the parametersmay be derived from an atlas of landmarks based on the patient's bonegeometry. This may be supplemented with the results of an anteriordrawer test, varus-valgus stability measurements, tissue attachmentestimates, and tissue condition measurements (e.g., stiffness), etc.Data may also be supplemented with intra-operatively acquired data, suchas joint distraction tests, instrumented tibial inserts, force sensinggloves, etc.

FIG. 5D shows the implant geometry parameters of the knee predictorequation. These parameters may include, for example, the femoral andtibial gaps, the distal or posterior radii, patellar geometry andalignment or stuffing, and the femoral anterior-posterior/lateral-medialplacement of the implant. It should be noted that there may be a numberof possible implants for a given patient (e.g., models, sizes, etc.). Assuch, different knee predictor equations may be designed with the samepatient-specific and tissue balance parameters, but different implantgeometry parameters. For example, a range of sizes may be represented bya set of key predictor equations. In some embodiments, the implantgeometry can be determined programmatically using anatomical modelingsoftware such as LIFEMOD™. Example techniques for optimizing parametersrelating to the anatomic and biomechanical fit of an implant or implantsystem implanted into the patient's joint are described in U.S. patentapplication Ser. No. 13/814,531, which was previously incorporatedherein by reference.

FIG. 5E shows the implant alignment and positioning parameters that maybe used in the knee predictor equation. As noted above, these may beused as variables during the optimization. As shown in FIG. 5E, exampleparameters include the femoral superior-inferior (S-I) position, thefemoral anterior-posterior (A-P) position, the femoral varus-valgus(V-V) position, the femoral internal-external (I-E) position, the tibialposterior slope, the tibial V-V position, the tibial I-E position, andthe tibial depth as determined by the extension gap.

FIG. 5F shows the response portion of the knee predictor equation. Theresponse may include a dataset comprising kinematics data and kineticsdata related to the knee. The kinematics data provides a measurement ofhow the kinematics of a specific patient and component set compared to aspecified goal. For example, the kinematics may measure theinternal-external rotation of the femoral component with respect to thetibia, and what that signature looks like over the flexion history of adeep knee bend event. This can then be compared to a goal measurement ofthe internal-external rotation. This concept can be extended to thepatella and other anatomy related to knee motion. The kinetics dataprovides a measure of the loads on the various components of the knee(e.g., LCL, MCL, etc.). As shown in FIG. 5F, the simulation derives thisdata for several different degrees of knee flexion (e.g., 30, 60, 90,120, etc.). Thus, for a given set of parameters on the left-hand side ofthe equation, a set of equations may be specified with differentresponse values.

FIG. 6 illustrates a process by which the optimization of the equationset may be performed, according to some embodiments. Starting at step605, the patient-specific parameters and the soft tissue balanceparameters are entered by the surgical staff. As noted above, thepatient-specific parameters may be derived based on any combination ofpre-operative and intra-operative data. The tissue balance parametersare measured by the surgeon (or default values may be used). At step610, the implant geometry or implant make, model, and manufacturer andproduct ID are entered by the surgical staff. In some embodiments, thesurgical staff may manually enter each of the implant geometryparameters. In other embodiments, the surgical staff may specify aparticular implant make, model, and/or size (e.g., SMITH & NEPHEW,GENESIS® II left femoral implant, size 6) and the appropriate implantparameters (e.g., geometry, dimensions, or other implantcharacteristics) can be retrieved from a local or remote database.

Continuing with reference to FIG. 6, at step 615, the equation set isselected based on the patient-specific parameters, soft tissueparameters and implant geometry. The equation set includes one or morepredictor equations with each equation providing a different responsevalue. Next, at step 620, the equation set is optimized to obtain theimplant alignment and position recommendation. In at least someembodiments, it may not be possible to perfectly solve all of theequations because the factors may impact on the various responses indifferent ways. As such, in some embodiments, the responses may beassociated with weighted values such that the optimization processaccords greater weight to certain responses than others. These weightedvalues may act as desirability factors or functions quantifying therelative importance of the various responses. For example, in someembodiments, the optimization is performed using a Goal Programming (GP)algorithm in which the weights of response variables are obtainedthrough a Group Decision Making (GDM) process. Finally, at step 625, theimplant alignment and position recommendation are visually depicted, forexample, on the Display 125 of the CASS 100.

In some embodiments, the relationship(s) between the factors andresponses may be defined by a set of trained neural networks rather thana series of equations. Similar statistical and modeling tools to thosedescribed above may be used to define and train the neural networks andthe factors used therein. In some embodiments, tools such asNEUROSOLUTIONS 6.0, available from NEURODIMENSIONS, INC. of Gainesville,Fla., may further facilitate the development and training of the neuralnetworks. In some embodiments, a database of information collected fromprevious orthopedic procedures or studies may be used to train theneural networks, and, as additional data is collected over time, theneural networks may be further refined to enhance the optimizationprocesses described herein. In some embodiments, kernel methods may beused to explore the relationship(s) between the factors and responses.Kernel-based learning algorithms may be used to solve complexcomputational problems, to detect and exploit complex patterns in thedata by clustering, classifying, etc.

In some embodiments, the relationship(s) between the factors andresponses may be defined by one or more trained support vector machines.Like some neural networks, a support vector machine may be trained torecognize patterns in existing data, such as data collected fromprevious orthopedic procedures or studies, and, once trained, used topredict responses for an orthopedic procedure for a particular patientbased on settings for certain factors.

Although the discussion above was directed to recommendations in thecontext of knee surgeries, the factors and responses used in thepredictor equations can be modified as needed based on the anatomy thatis the subject of the surgical procedure. For example, surgeries torepair a torn or injured anterior cruciate ligament (“ACL”) couldbenefit from the use of the CASS and CASSRR concepts described above.The application of robotic surgical systems to ACL surgeries aredescribed in PCT/US2019/048502, entitled “Robotic Assisted LigamentGraft Placement and Tensioning”, and filed on Aug. 28, 2019, which waspreviously incorporated by reference in its entirety.

Another surgical intervention that could benefit from the use of theCASS and CASSRR concepts described above is a high tibial osteotomy(“HTO”) procedure. In a HTO procedure, a cut in the tibia is made and awedge of bone may be removed from or added to the cut in the tibia tobetter align the tibia and femur in the knee joint. For example, CASSRRcould be used to optimize the amount of bone that is added or removed toachieved the desired kinetic response (i.e., unload the affectedcompartment to delay further cartilage damage). Additionally, changes intibial slope, which are difficult to plan, can be simulated androbotically implemented.

In the context of a THA procedure, the “implant alignment/position”factors discussed above with respect to FIG. 5E can be replaced withparameters such as cup inclination, cup anteversion, cup size, cupdepth, bearing type (traditional, ceramic on ceramic, dual mobility,resurfacing, etc.), femoral stem design, femoral stem version, combinedanteversion, femoral stem size, femoral stem offset (STD, HIGH), andfemoral head offset. Another component of implant alignment/positioncould be screw placement. The software may make recommendations for howmany screws should be used, the length and trajectory of each screw, aswell as problem areas to avoid (soft tissue, blood vessels, etc.) For ahip revision surgery, the “implant geometry” factors can be modified tomake recommendations for which type of acetabular or femoral componentswould best fill in the missing anatomy.

Furthermore, although the recommendation system was discussed above withrespect to the generation of intra-operative recommendations, it shouldbe noted that recommendations may also be applied during thepre-operative and post-operative stages of the episode of care. Forexample, based on pre-operative data and historical data, a recommendedsurgical plan can be developed. Similarly, if a surgical plan is alreadygenerated, recommendations may be generated based on changedcircumstances that occurred after the pre-operative data was generated.Post-surgery, the data gathered during the earlier stages of the episodeof care can be used to generate a recommended post-operativerehabilitation protocol (e.g., goals, exercises, etc.). Examples of datathat can affect the rehab protocol include, without limitation, implantmake and size, operative time, tourniquet time, tissue release, and theintraoperative flexion. Aside from the activities of the rehab protocol,the devices used for rehab and recovery could also be customized basedon the episode of care data. For example, in one embodiment, the episodeof care data is used to generate designs for custom shoe inserts thatcan be 3D printed for the patient. In addition to generatingpost-operative recommendations for the patient, the post-operativeepisode of care data can also be used as a feedback mechanism into theCASSRR to further refine the machine learning models used to providerecommendations for performing surgical procedures on other patients.

Slider Interfaces for Providing Interactive Anatomical Modeling Data

In some embodiments, as an alternative or supplement to the interfacesdescribed above, dynamic sliders can be used to depict variousmeasurements as shown in FIG. 7A. While FIG. 7A continues with theexample of knee implant alignment, it should be understood that thegeneral concept illustrated in FIG. 7A can be applied to various typesof measurements performed during surgery or a preoperative stage. Asdepicted in FIG. 7A, the surgeon either manually aligns the implant (asdepicted in image 705) or uses the optimization process described above(shown in image 710) to manipulate the implant on the display screen toproduce the responses 715. In this case, the responses 715 are shown asa plurality of sliders with settings corresponding to a plurality offlexion angles (30, 60, 90, and 120 degrees). These sliders are“dynamic” in the sense that they are updated in real-time as the surgeonchanges the alignment. Thus, for example, if the surgeon makes a manualmovement 705 of the implant, the responses 715 would be recalculated,and each slider would be updated accordingly.

FIG. 7B provides further illustration of the contents of the slidershowing the tibial implant sagittal alignment response. As depicted inFIG. 7B, the desired or preferred alignment configuration is 5 degreeflexion but the current flexion measurement is only 4 degrees. Anindication, prompt, or comment may be provided to indicate that flexionshould be altered (e.g., reduced or increased) in order to avoid cuttingthe fibula. The current implant alignment is depicted with a tick markon the lower portion of the slider, while the surgeon's target of 5degrees is shown as a tick mark on the upper portion of the slider. FDA510(k) limits (which define the allowable parameters to comply with FDAregulations) are shown with an outer bar overlaid on the slider.

In the example of FIG. 7B, the slider also includes an inner baroverlaid on the slider showing the surgeon's limits for the responsevalue. These limits can be derived or ascertained either by review andanalysis of certain historical data accessible from the CASS (i.e.,limits from past surgeries) or the surgical staff or other technicianmay input this information prior to surgery. In some embodiments, eachlimit is not provided explicitly; rather, the limit is derived from ageneral set of instructions provided by the surgeon. For example, in oneembodiment, the surgeon or the surgeon's staff provides a textualdescription of the surgeon's preferences. Then, a natural languageprocessing algorithm is applied to extract relevant information from thetext. Based on the extracted text, the rules for generating the text aregenerated. The following table provides an example set of rulesgenerated based on the shown input text.

Input Text Rules Wants to align to the neutral Fem Imp Val Ang Targ =Fem Imp Val Ang mechanical axis. Keep between 3 and LSL IF (Fem Val AngMeas < Fem Imp Val 7 degrees. Let surgeon decide if a Ang LSL) collet isnecessary. “Have a Fem Imp Val Ang Targ = Fem Val Ang Meas gravitationalpull towards 5 degrees” IF AND ( Fem Val Ang Meas ≥ Fem Imp Val but doesnot necessarily want a 5 Ang LSL, Fem Val Ang Meas ≤ 5° valgus) degreecollet. He said if it measures 9 Fem Imp Val Ang Targ = 7° valgus IF ANDdegrees, make it 7 degrees. If it (Fem Val Ang Meas > 7° valgus, Fem ValAng measures 7 degrees, make it 6 Meas ≤ 7° valgus + 3° valgus) degrees.If measured femur valgus Fem Imp Val Ang Targ = Fem Val Ang Meas + angleis less than 5 degrees, match 1° varus IF (Fem Val Ang Meas > 5.9°raster. If femur valgus angle is out of valgus, Fem Val Ang Meas ≤ 7°valgus) the ordinary (less than 3 degrees, more Fem Imp Val Ang Targ =Fem Val Ang Meas + than 7 degrees) contact surgeon. 3° varus IF (Fem ValAng Meas > 7° valgus + 3° valgus) Fem Imp Val Ang LSL = 3° valgus FemImp Val Ang USL = EMPTY

Operative Patient Care System

The general concepts of optimization may be extended to the entireepisode of care using an Operative Patient Care System 820 that uses thesurgical data, and other data from the Patient 805 and HealthcareProfessionals 830 to optimize outcomes and patient satisfaction asdepicted in FIG. 8.

Conventionally, pre-operative diagnosis, pre-operative surgicalplanning, intra-operative execution of a prescribed plan, andpost-operative management of total joint arthroplasty are based onindividual experience, published literature, and training knowledgebases of surgeons (ultimately, tribal knowledge of individual surgeonsand their ‘network’ of peers and journal publications) and their nativeability to make accurate intra-operative tactile discernment of“balance” and accurate manual execution of planar resections usingguides and visual cues. This existing knowledge base and execution islimited with respect to the outcomes optimization offered to patientsneeding care. For example, limits exist with respect to accuratelydiagnosing a patient to the proper, least-invasive prescribed care;aligning dynamic patient, healthcare economic, and surgeon preferenceswith patient-desired outcomes; executing a surgical plan resulting inproper bone alignment and balance, etc.; and receiving data fromdisconnected sources having different biases that are difficult toreconcile into a holistic patient framework. Accordingly, a data-driventool that more accurately models anatomical response and guides thesurgical plan can improve the existing approach.

The Operative Patient Care System 820 is designed to utilize patientspecific data, surgeon data, healthcare facility data, and historicaloutcome data to develop an algorithm that suggests or recommends anoptimal overall treatment plan for the patient's entire episode of care(preoperative, operative, and postoperative) based on a desired clinicaloutcome. For example, in one embodiment, the Operative Patient CareSystem 820 tracks adherence to the suggested or recommended plan, andadapts the plan based on patient/care provider performance. Once thesurgical treatment plan is complete, collected data is logged by theOperative Patient Care System 820 in a historical database. Thisdatabase is accessible for future patients and the development of futuretreatment plans. In addition to utilizing statistical and mathematicalmodels, simulation tools (e.g., LIFEMOD®) can be used to simulateoutcomes, alignment, kinematics, etc., based on a preliminary orproposed surgical plan, and reconfigure the preliminary or proposed planto achieve desired or optimal results according to a patient's profileor a surgeon's preferences. The Operative Patient Care System 820ensures that each patient is receiving personalized surgical andrehabilitative care, thereby improving the chance of successful clinicaloutcomes and lessening the economic burden on the facility associatedwith near-term revision.

In some embodiments, the Operative Patient Care System 820 employs adata collecting and management method to provide a detailed surgicalcase plan with distinct steps that are monitored and/or executed using aCASS 100. The performance of the user(s) is calculated at the completionof each step and can be used to suggest changes to the subsequent stepsof the case plan. Case plan generation relies on a series of input datathat is stored on a local or cloud-storage database. Input data can berelated to both the current patient undergoing treatment and historicaldata from patients who have received similar treatment(s).

A Patient 805 provides inputs such as Current Patient Data 810 andHistorical Patient Data 815 to the Operative Patient Care System 820.Various methods generally known in the art may be used to gather suchinputs from the Patient 805. For example, in some embodiments, thePatient 805 fills out a paper or digital survey that is parsed by theOperative Patient Care System 820 to extract patient data. In otherembodiments, the Operative Patient Care System 820 may extract patientdata from existing information sources, such as electronic medicalrecords (EMRs), health history files, and payer/provider historicalfiles. In still other embodiments, the Operative Patient Care System 820may provide an application program interface (API) that allows theexternal data source to push data to the Operative Patient Care System.For example, the Patient 805 may have a mobile phone, wearable device,or other mobile device that collects data (e.g., heart rate, pain ordiscomfort levels, exercise or activity levels, or patient-submittedresponses to the patient's adherence with any number of pre-operativeplan criteria or conditions) and provides that data to the OperativePatient Care System 820. Similarly, the Patient 805 may have a digitalapplication on his or her mobile or wearable device that enables data tobe collected and transmitted to the Operative Patient Care System 820.

Current Patient Data 810 can include, but is not limited to, activitylevel, preexisting conditions, comorbidities, prehab performance, healthand fitness level, pre-operative expectation level (relating tohospital, surgery, and recovery), a Metropolitan Statistical Area (MSA)driven score, genetic background, prior injuries (sports, trauma, etc.),previous joint arthroplasty, previous trauma procedures, previous sportsmedicine procedures, treatment of the contralateral joint or limb, gaitor biomechanical information (back and ankle issues), levels of pain ordiscomfort, care infrastructure information (payer coverage type, homehealth care infrastructure level, etc.), and an indication of theexpected ideal outcome of the procedure.

Historical Patient Data 815 can include, but is not limited to, activitylevel, preexisting conditions, comorbidities, prehab performance, healthand fitness level, pre-operative expectation level (relating tohospital, surgery, and recovery), a MSA driven score, geneticbackground, prior injuries (sports, trauma, etc.), previous jointarthroplasty, previous trauma procedures, previous sports medicineprocedures, treatment of the contralateral joint or limb, gait orbiomechanical information (back and ankle issues), levels or pain ordiscomfort, care infrastructure information (payer coverage type, homehealth care infrastructure level, etc.), expected ideal outcome of theprocedure, actual outcome of the procedure (patient reported outcomes[PROs], survivorship of implants, pain levels, activity levels, etc.),sizes of implants used, position/orientation/alignment of implants used,soft-tissue balance achieved, etc.

Healthcare Professional(s) 830 conducting the procedure or treatment mayprovide various types of data 825 to the Operative Patient Care System820. This Healthcare Professional Data 825 may include, for example, adescription of a known or preferred surgical technique (e.g., CruciateRetaining (CR) vs Posterior Stabilized (PS), up- vs down-sizing,tourniquet vs tourniquet-less, femoral stem style, preferred approachfor THA, etc.), the level of training of the Healthcare Professional(s)830 (e.g., years in practice, fellowship trained, where they trained,whose techniques they emulate), previous success level includinghistorical data (outcomes, patient satisfaction), and the expected idealoutcome with respect to range of motion, days of recovery, andsurvivorship of the device. The Healthcare Professional Data 825 can becaptured, for example, with paper or digital surveys provided to theHealthcare Professional 830, via inputs to a mobile application by theHealthcare Professional, or by extracting relevant data from EMRs. Inaddition, the CASS 100 may provide data such as profile data (e.g., aPatient Specific Knee Instrument Profile) or historical logs describinguse of the CASS during surgery.

Information pertaining to the facility where the procedure or treatmentwill be conducted may be included in the input data. This data caninclude, without limitation, the following: Ambulatory Surgery Center(ASC) vs hospital, facility trauma level, Comprehensive Care for JointReplacement Program (CJR) or bundle candidacy, a MSA driven score,community vs metro, academic vs non-academic, postoperative networkaccess (Skilled Nursing Facility [SNF] only, Home Health, etc.),availability of medical professionals, implant availability, andavailability of surgical equipment.

These facility inputs can be captured by, for example and withoutlimitation, Surveys (Paper/Digital), Surgery Scheduling Tools (e.g.,apps, Websites, Electronic Medical Records [EMRs], etc.), Databases ofHospital Information (on the Internet), etc. Input data relating to theassociated healthcare economy including, but not limited to, thesocioeconomic profile of the patient, the expected level ofreimbursement the patient will receive, and if the treatment is patientspecific may also be captured.

These healthcare economic inputs can be captured by, for example andwithout limitation, Surveys (Paper/Digital), Direct Payer Information,Databases of Socioeconomic status (on the Internet with zip code), etc.Finally, data derived from simulation of the procedure is captured.Simulation inputs include implant size, position, and orientation.Simulation can be conducted with custom or commercially availableanatomical modeling software programs (e.g., LIFEMOD®, AnyBody, orOpenSIM). It is noted that the data inputs described above may not beavailable for every patient, and the treatment plan will be generatedusing the data that is available.

Prior to surgery, the Patient Data 810, 815 and Healthcare ProfessionalData 825 may be captured and stored in a cloud-based or online database(e.g., the Surgical Data Server 180 shown in FIG. 2C). Informationrelevant to the procedure is supplied to a computing system via wirelessdata transfer or manually with the use of portable media storage. Thecomputing system is configured to generate a case plan for use with aCASS 100. Case plan generation will be described hereinafter. It isnoted that the system has access to historical data from previouspatients undergoing treatment, including implant size, placement, andorientation as generated by a computer-assisted, patient-specific kneeinstrument (PSKI) selection system, or automatically by the CASS 100itself. To achieve this, case log data is uploaded to the historicaldatabase by a surgical sales rep or case engineer using an onlineportal. In some embodiments, data transfer to the online database iswireless and automated.

Historical data sets from the online database are used as inputs to amachine learning model such as, for example, a recurrent neural network(RNN) or other form of artificial neural network. As is generallyunderstood in the art, an artificial neural network functions similar toa biologic neural network and is comprised of a series of nodes andconnections. The machine learning model is trained to predict one ormore values based on the input data. For the sections that follow, it isassumed that the machine learning model is trained to generate predictorequations. These predictor equations may be optimized to determine theoptimal size, position, and orientation of the implants to achieve thebest outcome or satisfaction level.

FIG. 9 shows an example of using seeding data to determine the predictorequation from machine learning according to some embodiments. Inputsignals comprising information from the online database (describedpreviously) are introduced to the system as input nodes. Each input nodeis connected to a series of downstream nodes for calculation in thehidden layer. Each node is generally represented by a real number(typically between 0 and 1), but the connections between nodes also haveweighting values that change as the system ‘learns’. To train thesystem, a set of seeding or training data is supplied with associatedknown output values. Seeding data is iteratively passed to the systemand the inter-node weighting values are altered until the systemprovides a result that matches the known output. In this embodiment, theweighting values in the hidden layer of the network are captured in aweighting matrix that can be used to characterize the performance of thesurgery. The weighting matrix values are used as coefficients in apredictor equation that relates database inputs to outcomes andsatisfaction. Initially, the RNN will be trained with seeding datadeveloped through clinical studies and registry data. Once a sufficientnumber of cases have been established in the database, the system willuse historical data for system improvement and maintenance. Note thatthe use of a RNN will act as a filter to determine which input data havea large effect on outputs. The system operator may choose a sensitivitythreshold so that input data that does not have a significant effect onoutput can be disregarded and no longer captured for analysis.

FIG. 10 shows embodiments 1000 of how the Operative Patient Care System820 can be used in surgery, according to some embodiments. Starting atstep 1005, the surgical staff begins the surgery with the CASS. The CASSmay be an image-based or imageless system, as is generally understood inthe art. Regardless of the type of system employed, at step 1010, thesurgical staff can access or acquire a 3D representation of thepatient's relevant body anatomy (traditional probe painting, 3D imagingmapped with references, visual edge detection, etc.). In many cases, the3D representation of the anatomy can be mathematically accomplished bycapturing a series of Cartesian coordinates that represent the tissuesurface. Example file formats include, without limitation, .stl, .stp,.sur, .igs, .wrl, .xyz, etc. The 3D representation of the patient'srelevant body anatomy may be generated pre-operatively based on imagedata, for example, or the 3D representation can be generatedintraoperatively using the CASS.

Certain input data for the current patient can be loaded on a computingsystem, for example, through wireless data transfer or the use ofportable storage media. The input file is read into the neural networkand a resulting predictor equation is generated at step 1015. Next, atstep 1020, global optimization of the predictor equation (e.g., usingdirect Monte-Carlo sampling, stochastic tunneling, parallel tempering,etc.) is conducted to determine the optimal size, position, andorientation of the implants to achieve the best outcome or satisfactionlevel and determine the corresponding resections to be performed basedon the implant size, position, and orientation. During the optimizationphase, the system operator may choose to ignore aspects of the equation.For example, if the clinician feels that inputs relating to thepatient's economic status are not relevant, the coefficients related tothese inputs can be removed from the equation (e.g., based on inputprovided through a GUI of the CASS 100).

In some embodiments, the predictor equation is not calculated using aRNN, but instead with a design of experiments (DOE) method. A DOE willprovide sensitivity values relating each of the input values to anoutput value. Significant inputs are combined in a mathematical formulathat was previously described as a predictor equation.

Regardless of how the predictor equation is configured or determined,optimization of this equation can provide recommended, preferred oroptimized implant positioning, for example, in the form of a homogenoustransformation matrix. The transform mathematically sizes and orientsthe implant components relative to the patient anatomy. Booleanintersection of the implant geometry and patient anatomy creates avolumetric representation of the bone to be removed. This volume isdefined as the “cutting envelope.” In many commercially availableorthopedic robotic surgical systems, the bone removal tool is trackedrelative to the patient anatomy (with optical tracking and othermethods). Using position feedback control, the speed or depth of thecutting tool is modulated based on the tool's position within thecutting envelope (i.e., the cutting tool will spin when the position ofthe tool end is within the cutting envelope and will stop or retractwhen its position is outside of the cutting envelope).

Once the procedure is complete, at step 1025, all patient data andavailable outcome data, including the implant size, position andorientation determined by the CASS, are collected and stored in thehistorical database. Any subsequent calculation of the target equationvia the RNN will include the data from the previous patient in thismanner, allowing for continuous improvement of the system.

In addition to, or as an alternative to determining implant positioning,in some embodiments, the predictor equation and associated optimizationcan be used to generate the resection planes for use with a PSKI system.When used with a PSKI system, the predictor equation computation andoptimization are completed prior to surgery. Patient anatomy isestimated using medical image data (x-ray, CT, MRI). Global optimizationof the predictor equation can provide an ideal size and position of theimplant components. Boolean intersection of the implant components andpatient anatomy is defined as the resection volume. PSKI can be producedto remove the optimized resection envelope. In this embodiment, thesurgeon cannot alter the surgical plan intraoperatively.

The surgeon may choose to alter the surgical case plan at any time priorto or during the procedure. If the surgeon elects to deviate from thesurgical case plan, the altered size, position, and/or orientation ofthe component(s) is locked, and the global optimization is refreshedbased on the new size, position, and/or orientation of the component(s)(using the techniques previously described) to find the new idealposition of the other component(s) and the corresponding resectionsneeded to be performed to achieve the newly optimized size, positionand/or orientation of the component(s). For example, if the surgeondetermines that the size, position and/or orientation of the femoralimplant in a TKA needs to be updated or modified intraoperatively, thefemoral implant position is locked relative to the anatomy, and the newoptimal position of the tibia will be calculated (via globaloptimization) considering the surgeon's changes to the femoral implantsize, position and/or orientation. Furthermore, if the surgical systemused to implement the case plan is robotically assisted (e.g., as withNAVIO® or the MAKO Rio), bone removal and bone morphology during thesurgery can be monitored in real time. If the resections made during theprocedure deviate from the surgical plan, the subsequent placement ofadditional components may be optimized by the processor taking intoaccount the actual resections that have already been made.

FIG. 11A illustrates how the Operative Patient Care System 820 (FIG. 8)can be adapted for performing case plan matching services. In thisexample, data 810 is captured relating to the current patient 805 and iscompared to all or portions of a historical database of patient data andassociated outcomes 815. For example, the surgeon may elect to comparethe plan for the current patient against a subset of the historicaldatabase. Data in the historical database can be filtered to include,for example, only data sets with favorable outcomes, data setscorresponding to historical surgeries of patients with profiles that arethe same or similar to the current patient profile, data setscorresponding to a particular surgeon, data sets corresponding to aparticular aspect of the surgical plan (e.g., only surgeries where aparticular ligament is retained), or any other criteria selected by thesurgeon or medical professional. If, for example, the current patientdata matches or is correlated with that of a previous patient whoexperienced a good outcome, the case plan from the previous patient canbe accessed and adapted or adopted for use with the current patient. Thepredictor equation may be used in conjunction with an intra-operativealgorithm that identifies or determines the actions associated with thecase plan. Based on the relevant and/or preselected information from thehistorical database, the intra-operative algorithm determines a seriesof recommended actions for the surgeon to perform. Each execution of thealgorithm produces the next action in the case plan. If the surgeonperforms the action, the results are evaluated. The results of thesurgeon's performing the action are used to refine and update inputs tothe intra-operative algorithm for generating the next step in the caseplan. Once the case plan has been fully executed, all data associatedwith the case plan, including any deviations performed from therecommended actions by the surgeon, are stored in the database ofhistorical data. In some embodiments, the system utilizes preoperative,intraoperative, or postoperative modules in a piecewise fashion, asopposed to the entire continuum of care. In other words, caregivers canprescribe any permutation or combination of treatment modules includingthe use of a single module. These concepts are illustrated in FIG. 11Band can be applied to any type of surgery utilizing the CASS 100.

Surgery Process Display

As noted above with respect to FIGS. 1-2C, the various components of theCASS 100 generate detailed data records during surgery. The CASS 100 cantrack and record various actions and activities of the surgeon duringeach step of the surgery and compare actual activity to thepre-operative or intraoperative surgical plan. In some embodiments, asoftware tool may be employed to process this data into a format wherethe surgery can be effectively “played-back.” For example, in oneembodiment, one or more GUIs may be used that depict all of theinformation presented on the Display 125 during surgery. This can besupplemented with graphs and images that depict the data collected bydifferent tools. For example, a GUI that provides a visual depiction ofthe knee during tissue resection may provide the measured torque anddisplacement of the resection equipment adjacent to the visual depictionto better provide an understanding of any deviations that occurred fromthe planned resection area. The ability to review a playback of thesurgical plan or toggle between different aspects of the actual surgeryvs. the surgical plan could provide benefits to the surgeon and/orsurgical staff, allowing such persons to identify any deficiencies orchallenging aspects of a surgery so that they can be modified in futuresurgeries. Similarly, in academic settings, the aforementioned GUIs canbe used as a teaching tool for training future surgeons and/or surgicalstaff. Additionally, because the data set effectively records manyaspects of the surgeon's activity, it may also be used for other reasons(e.g., legal or compliance reasons) as evidence of correct or incorrectperformance of a particular surgical procedure.

Over time, as more and more surgical data is collected, a rich libraryof data may be acquired that describes surgical procedures performed forvarious types of anatomy (knee, shoulder, hip, etc.) by differentsurgeons for different patients. Moreover, aspects such as implant typeand dimension, patient demographics, etc., can further be used toenhance the overall dataset. Once the dataset has been established, itmay be used to train a machine learning model (e.g., RNN) to makepredictions of how surgery will proceed based on the current state ofthe CASS 100.

Training of the machine learning model can be performed as follows. Theoverall state of the CASS 100 can be sampled over a plurality of timeperiods for the duration of the surgery. The machine learning model canthen be trained to translate a current state at a first time period to afuture state at a different time period. By analyzing the entire stateof the CASS 100 rather than the individual data items, any causaleffects of interactions between different components of the CASS 100 canbe captured. In some embodiments, a plurality of machine learning modelsmay be used rather than a single model. In some embodiments, the machinelearning model may be trained not only with the state of the CASS 100,but also with patient data (e.g., captured from an EMR) and anidentification of members of the surgical staff. This allows the modelto make predictions with even greater specificity. Moreover, it allowssurgeons to selectively make predictions based only on their ownsurgical experiences if desired.

In some embodiments, predictions or recommendations made by theaforementioned machine learning models can be directly integrated intothe surgical workflow. For example, in some embodiments, the SurgicalComputer 150 may execute the machine learning model in the backgroundmaking predictions or recommendations for upcoming actions or surgicalconditions. A plurality of states can thus be predicted or recommendedfor each period. For example, the Surgical Computer 150 may predict orrecommend the state for the next 5 minutes in 30 second increments.Using this information, the surgeon can utilize a “process display” viewof the surgery that allows visualization of the future state. Forexample, FIG. 11C depicts a series of images that may be displayed tothe surgeon depicting the implant placement interface. The surgeon cancycle through these images, for example, by entering a particular timeinto the display 125 of the CASS 100 or instructing the system toadvance or rewind the display in a specific time increment using atactile, oral, or other instruction. In one embodiment, the processdisplay can be presented in the upper portion of the surgeon's field ofview in the AR HMD. In some embodiments, the process display can beupdated in real-time. For example, as the surgeon moves resection toolsaround the planned resection area, the process display can be updated sothat the surgeon can see how his or her actions are affecting the otheraspects of the surgery.

In some embodiments, rather than simply using the current state of theCASS 100 as an input to the machine learning model, the inputs to themodel may include a planned future state. For example, the surgeon mayindicate that he or she is planning to make a particular bone resectionof the knee joint. This indication may be entered manually into theSurgical Computer 150 or the surgeon may verbally provide theindication. The Surgical Computer 150 can then produce a film stripshowing the predicted effect of the cut on the surgery. Such a filmstrip can depict over specific time increments how the surgery will beaffected, including, for example, changes in the patient's anatomy,changes to implant position and orientation, and changes regardingsurgical intervention and instrumentation, if the contemplated course ofaction were to be performed. A surgeon or medical professional caninvoke or request this type of film strip at any point in the surgery topreview how a contemplated course of action would affect the surgicalplan if the contemplated action were to be carried out.

It should be further noted that, with a sufficiently trained machinelearning model and robotic CASS, various aspects of the surgery can beautomated such that the surgeon only needs to be minimally involved, forexample, by only providing approval for various steps of the surgery.For example, robotic control using arms or other means can be graduallyintegrated into the surgical workflow over time with the surgeon slowlybecoming less and less involved with manual interaction versus robotoperation. The machine learning model in this case can learn whatrobotic commands are required to achieve certain states of theCASS-implemented plan. Eventually, the machine learning model may beused to produce a film strip or similar view or display that predictsand can preview the entire surgery from an initial state. For example,an initial state may be defined that includes the patient information,the surgical plan, implant characteristics, and surgeon preferences.Based on this information, the surgeon could preview an entire surgeryto confirm that the CASS-recommended plan meets the surgeon'sexpectations and/or requirements. Moreover, because the output of themachine learning model is the state of the CASS 100 itself, commands canbe derived to control the components of the CASS to achieve eachpredicted state. In the extreme case, the entire surgery could thus beautomated based on just the initial state information.

Using Anatomical Modeling Software for Pre-Operative Planning

In some embodiments, anatomical modeling software, such as LIFEMOD™, canbe used to develop a pre-operative or intraoperative plan to guidesurgery. For example, in the context of hip surgery, if the anatomicalmodeling software has knowledge of relationship between the spine andthe pelvis throughout a variety of functional activities, then thesoftware can better predict an optimal implant position. Studies haveshown that individuals who have limited or abnormal spino-pelvicmobility are at a higher risk for dislocation. For these patients,surgeons recommend taking lateral radiographs in several positions(e.g., standing, sitting, flexed-standing) in order to understand howthe spine and pelvis interact during a variety of activities. Theseimages can feed into a 3D biomechanical simulation to better predict theoptimal implant positions and orientations. Additionally, as analternative to the manual process of taking radiographs, the anatomicalmodeling software may also be used to simulate the positions of thelumbar spine and pelvis throughout a range of activities. In the contextof knee surgery, if the anatomical modeling software has knowledge ofthe relationship among the mechanical axes of the joint, the condylaraxis, and the central axes of the femur and tibia, and the existingflexion and extension gaps, the software can better determine howchanges in the size and pose (position and orientation) of the implantcomponents can affect the mechanics of the replacement knee. Morespecifically, if the software incorporates the relationship betweenthese variables throughout the range of motion and exemplary forces of agiven patient activity, the implant performance can be modeled.

FIGS. 12A-12C provide some outputs that the anatomical modeling softwaremay use to visually depict the results of modeling hip activity with theanatomical modeling software. FIGS. 12A and 12B show hip range of motion(ROM) plots. In this case, the anatomical modeling software may beemployed to perform a ROM “exam” by placing the patient in variouspositions and modeling movement of the hip when the patient's leg movesin different positions. For example, the software can virtually simulatethe positions and orientation of the implants in relation to bonyanatomy throughout a variety of activities that a patient may experiencepost-surgery. These may include the standard stability checks that areperformed during a total hip procedure, or could even include activitiesthat pose a high risk for impingement and dislocation (crossing legswhile seated, deep flexion while sitting, hyperextension while standing,etc.). Following performance of the exam, the collected ROM data can bepresented on a ROM plot as shown in FIG. 12A. Additionally, theanatomical modeling software may identify any impinged ROM whereabnormal and wearing contact exists between the patient's anatomy andthe implant components. As shown in FIG. 12B, after the unimpinged ROMis determined, it may be graphically overlaid on a 3D model of thepatient's anatomy.

FIG. 12C shows 2D graphs that demonstrate a recommendation for adesirable or “safe” range of positions for seating a hip implant in theacetabulum. In this case, the anatomical modeling software may be usedto identify a safe range of placement locations by modeling functionalactivities to the point of failure. For example, initially, theanatomical modeling software may assume that the implant can be placedat any location within a large bounding box surrounding the anatomy ofinterest. Then, for each possible implant position, the anatomicalmodeling software may be used to test whether the position creates afailure of the anatomy or implant under normal functional activities. Inthe event of failure, a position is discarded. Once all the possiblepoints have been evaluated, the remaining points are considered “safe”for implant placement. As shown in FIG. 12B, for hip surgeries the safepositions may be indicated by a graph of abduction versus anteversion.In this case, the Lewinnek Safe Zone is superimposed on the graph. As iscommonly understood in the art, the Lewinnek Safe Zone is based on theclinical observation that dislocation is less likely to occur if theacetabular cup is placed within 30 degrees-50 degrees of abduction and5-25 degrees of anteversion. However, in this case, some of thepatient's “safe” positions (depicted in “red”) fall outside the LewinnekSafe Zone. Thus, the surgeon is given more flexibility to deviate fromstandard recommendations based on the features of the patient's anatomy.

An additional output of the anatomical modeling software may be 3Drenderings that display the final implant components in relation to thebone. In some embodiments, the 3D rendering may be displayed in aninterface that allows the surgeon to rotate around the entire image andview the rendering from different perspectives. The interface may allowthe surgeon to articulate the joint to visualize how implants willperform and identify where impingement, misalignment, excessive strain,or other problems may occur. The interface may include functionalitythat allows the surgeon to hide certain implant components or anatomicalfeatures in order to best visualize certain areas of the patientanatomy. For example, for a knee implant, the interface may allow thesurgeon to hide the femoral component and only show the bearing surfaceand tibial component in the visualization. FIG. 12D provides anexemplary visualization in the context of a hip implant.

In some embodiments, the anatomical modeling software may provide ananimation that shows the positions of the implants and bone as thepatient performs different physical activities. For example, in thecontext of a knee surgery, a deep knee bend can be presented. For a hipsurgery, the spine and pelvis can be shown during walking, sitting,standing, and other activities that may represent a challenge to theimplants.

Revision hip or knee arthroplasty surgery involves the removal of one ormore existing hip/knee implants and replacing the removed implants withnew implants in a single surgical procedure. In some embodiments, thepreoperative planning stage can anticipate handling of osteophytes orother tasks beyond planar resections needed to receive a new implant.For example, in some embodiments, for a revision surgery, an output ofthe anatomical modeling software may be a 3D image or bone map showingthe placement of each of the components, as well as problem areas thatmay require special preparation by the surgeon. Areas that would preventimplants from being fully seated may be highlighted by the software inthe surgical plan to show the surgeon which areas will require bone tobe removed. This software may be interactive, allowing the surgeon tovirtually “ream” or “burr” bone away in order to optimally prepare thebone to receive the implants. This may be performed, for example, byallowing the surgeon/engineer to selectively remove individual pixels orgroups of pixels from an X-ray image or a 3D representation by touchingthe area or using a “virtual burr” component of the interface. In someembodiments, images of mixed modalities can be registered and overlaidto provide a view of the structure of the anatomical area of interest,the anatomy, etc. In some embodiments, the anatomical modeling softwaremay provide a recommended area for burring away the bone. For example, amachine learning model may be trained to identify areas for burringbased on how the surgeon (or other surgeons) performed burring in thepast. Then, using the x-ray image or other patient measurements asinput, the machine learning model can input the recommended burring areafor review by the surgeon during the virtual burring procedure. In someembodiments, the virtual burring process can be performed interactivelywith other aspects of the anatomical modeling discussed above. Thisburring process may also be used with primary arthroplasty.

Using the Point Probe to Acquire High-Resolution of Key Areas During HipSurgeries

Use of the point probe is described in U.S. patent application Ser. No.14/955,742 entitled “Systems and Methods for Planning and PerformingImage Free Implant Revision Surgery,” the entirety of which isincorporated herein by reference. Briefly, an optically tracked pointprobe may be used to map the actual surface of the target bone thatneeds a new implant. Mapping is performed after removal of the defectiveor worn-out implant, as well as after removal of any diseased orotherwise unwanted bone. A plurality of points is collected on the bonesurfaces by brushing or scraping the entirety of the remaining bone withthe tip of the point probe. This is referred to as tracing or “painting”the bone. The collected points are used to create a three-dimensionalmodel or surface map of the bone surfaces in the computerized planningsystem. The created 3D model of the remaining bone is then used as thebasis for planning the procedure and necessary implant sizes. Analternative technique that uses X-rays to determine a 3D model isdescribed in U.S. patent application Ser. No. 16/387,151, filed Apr. 17,2019 and entitled “Three Dimensional Guide with Selective BoneMatching,” the entirety of which is incorporated herein by reference.

For hip applications, the point probe painting can be used to acquirehigh resolution data in key areas such as the acetabular rim andacetabular fossa. This can allow a surgeon to obtain a detailed viewbefore beginning to ream. For example, in one embodiment, the pointprobe may be used to identify the floor (fossa) of the acetabulum. As iswell understood in the art, in hip surgeries, it is important to ensurethat the floor of the acetabulum is not compromised during reaming so asto avoid destruction of the medial wall. If the medial wall wereinadvertently destroyed, the surgery would require the additional stepof bone grafting. With this in mind, the information from the pointprobe can be used to provide operating guidelines to the acetabularreamer during surgical procedures. For example, the acetabular reamermay be configured to provide haptic feedback to the surgeon when he orshe reaches the floor or otherwise deviates from the surgical plan.Alternatively, the CASS 100 may automatically stop the reamer when thefloor is reached or when the reamer is within a threshold distance.

As an additional safeguard, the thickness of the area between theacetabulum and the medial wall could be estimated. For example, once theacetabular rim and acetabular fossa has been painted and registered tothe pre-operative 3D model, the thickness can readily be estimated bycomparing the location of the surface of the acetabulum to the locationof the medial wall. Using this knowledge, the CASS 100 may providealerts or other responses in the event that any surgical activity ispredicted to protrude through the acetabular wall while reaming.

The point probe may also be used to collect high resolution data ofcommon reference points used in orienting the 3D model to the patient.For example, for pelvic plane landmarks like the ASIS and the pubicsymphysis, the surgeon may use the point probe to paint the bone torepresent a true pelvic plane. Given a more complete view of theselandmarks, the registration software has more information to orient the3D model.

The point probe may also be used to collect high-resolution datadescribing the proximal femoral reference point that could be used toincrease the accuracy of implant placement. For example, therelationship between the tip of the Greater Trochanter (GT) and thecenter of the femoral head is commonly used as a reference point toalign the femoral component during hip arthroplasty. The alignment ishighly dependent on proper location of the GT; thus, in someembodiments, the point probe is used to paint the GT to provide a highresolution view of the area. Similarly, in some embodiments, it may beuseful to have a high-resolution view of the Lesser Trochanter (LT). Forexample, during hip arthroplasty, the Don Classification helps to selecta stem that will maximize the ability of achieving a press-fit duringsurgery to prevent micromotion of femoral components post-surgery andensure optimal bony ingrowth. As is generally understood in the art, theDorr Classification measures the ratio between the canal width at the LTand the canal width 10 cm below the LT. The accuracy of theclassification is highly dependent on the correct location of therelevant anatomy. Thus, it may be advantageous to paint the LT toprovide a high-resolution view of the area.

In some embodiments, the point probe is used to paint the femoral neckto provide high-resolution data that allows the surgeon to betterunderstand where to make the neck cut. The navigation system can thenguide the surgeon as they perform the neck cut. For example, asunderstood in the art, the femoral neck angle is measured by placing oneline down the center of the femoral shaft and a second line down thecenter of the femoral neck. Thus, a high-resolution view of the femoralneck (and possibly the femoral shaft as well) would provide a moreaccurate calculation of the femoral neck angle.

High-resolution femoral head neck data could also be used for anavigated resurfacing procedure where the software/hardware aids thesurgeon in preparing the proximal femur and placing the femoralcomponent. As is generally understood in the art, during hipresurfacing, the femoral head and neck are not removed; rather, the headis trimmed and capped with a smooth metal covering. In this case, itwould be advantageous for the surgeon to paint the femoral head and capso that an accurate assessment of their respective geometries can beunderstood and used to guide trimming and placement of the femoralcomponent.

Registration of Pre-Operative Data to Patient Anatomy Using the PointProbe

As noted above, in some embodiments, a 3D model is developed during thepre-operative stage based on 2D or 3D images of the anatomical area ofinterest. In such embodiments, registration between the 3D model and thesurgical site is performed prior to the surgical procedure. Theregistered 3D model may be used to track and measure the patient'sanatomy and surgical tools intraoperatively.

During the surgical procedure, landmarks are acquired to facilitateregistration of this pre-operative 3D model to the patient's anatomy.For knee procedures, these points could comprise the femoral headcenter, distal femoral axis point, medial and lateral epicondyles,medial and lateral malleolus, proximal tibial mechanical axis point, andtibial A/P direction. For hip procedures these points could comprise theanterior superior iliac spine (ASIS), the pubic symphysis, points alongthe acetabular rim and within the hemisphere, the greater trochanter(GT), and the lesser trochanter (LT).

In a revision surgery, the surgeon may paint certain areas that containanatomical defects to allow for better visualization and navigation ofimplant insertion. These defects can be identified based on analysis ofthe pre-operative images. For example, in one embodiment, eachpre-operative image is compared to a library of images showing “healthy”anatomy (i.e., without defects). Any significant deviations between thepatient's images and the healthy images can be flagged as a potentialdefect. Then, during surgery, the surgeon can be warned of the possibledefect via a visual alert on the display 125 of the CASS 100. Thesurgeon can then paint the area to provide further detail regarding thepotential defect to the Surgical Computer 150.

In some embodiments, the surgeon may use a non-contact method forregistration of bony anatomy intra-incision. For example, in oneembodiment, laser scanning is employed for registration. A laser stripeis projected over the anatomical area of interest and the heightvariations of the area are detected as changes in the line. Othernon-contact optical methods, such as white light inferometry orultrasound, may alternatively be used for surface height measurement orto register the anatomy. For example, ultrasound technology may bebeneficial where there is soft tissue between the registration point andthe bone being registered (e.g., ASIS, pubic symphysis in hipsurgeries), thereby providing for a more accurate definition of anatomicplanes.

Surgical Navigation with Mixed Reality Visualization

In some embodiments, a surgical navigation system utilizes an augmentedreality (AR) or mixed reality (MR) visualization system to furtherassist a surgeon during robotically-assisted surgery. Conventionalsurgical navigation can be enhanced with AR by using graphical andinformational overlays (e.g., holographic or heads up displays/HUD) toguide surgical execution. An exemplary system allows for theimplementation of multiple headsets to share the same mixed or differentreality experience in real-time. In a multi-user use scenario, multipleuser profiles can be implemented for selective AR display. This canallow headsets to work together or independently, displaying differentsubsets of information to each user.

Embodiments utilizing AR/MR include a surgical system for operating in asurgical environment to enhance surgery through vision tracking thatincludes head mounted displays (HMDs) worn by one or more individualsperforming operational functions. For example, a surgeon may have an HMDand some or all of the nurses or lab technicians assisting the surgeon(or residents, other surgeons etc.) may have their own HMD. By usingHMDs, a surgeon can view information pertaining to the surgery,including information traditionally associated with robotic surgicalenhancement, without requiring the surgeon to shift his vision fieldaway from the patient. This can make the surgery faster because thesurgeon does not need to context switch between the display and thepatient. In some embodiments, a surgeon can selectively be shown avirtual holographic monitor that mirrors the display of a conventionalcart-mounted LCD screen during surgery. The HMD interface can allow thesurgeon to move the holographic monitor to appear fixed in space at anylocation in space she chooses, such as next to exposed patient tissue infront of surgical drapes.

A variety of types of HMDs can be used in some embodiments. Generally,an HMD includes a headpiece that is worn on a user's head and acommunication interface. The communication interface can be wired, suchas USB, serial port, SATA, or proprietary communication interfaces, orpreferably wireless, such as Wi-Fi or Bluetooth (but timing andbandwidth constraints can restrict practical choices to some of thefaster conventional communication interfaces, such as Wi-Fi or USB 3.0).An exemplary HMD also has a power source (such as battery or hardwiredpower connector), an onboard computer (including a processor, GPU, RAM,and non-volatile data and instruction memory), and one or more displaysfor superimposing information into the user's field of view. Anexemplary HMD may also include an array of cameras (which may includeoptical and IR sensors and illumination sources) that capture 3-Dimagery of the environment. In some embodiments, the HMD or an externalprocessor may create a model of the user's environment using imageprocessing algorithms that identify important features of theenvironment and by processing stereoscopic or IR data to create a 3Dmodel of the environment. By calibrating the HMD's display to the user'sfield of view, information displayed on a holographic display (e.g.,using direct retinal or semi-reflective projection) can be reliablysuperimposed onto the user's field of view to augment the user's view ofthe environment.

The HMDs worn by surgical staff can include commercially available,off-the-shelf HMDs, such as the Oculus Rift™, Microsoft HoloLens™,Google Glass™, Magic Leap One™, or custom designed hardware for thesurgical environment. In some embodiments, supplemental hardware isadded to a commercially available HMD to enhance it for the surgicalenvironment using off-the-shelf HMD components and custom hardware. Insome embodiments, HMD hardware can be integrated into traditionalsurgical hoods and face shields allowing the HMD to serve as personalprotective equipment and to allow information to be displayed byreflecting light off of a face shield that a surgeon already is familiarwith wearing. HMD technology on the market has a variety of approachesto providing a mixed reality environment for a user. For example,virtual-reality headsets, such as the Oculus Rift™, HTC Vive™, SonyPlayStation VR™, or Samsung Gear VR™, obstruct the user's naturalvision, replacing the entire visual field of the user with astereoscopic screen to create a 3D environment. These systems can useone or more cameras to recreate an enhanced version of thethree-dimensional environment to be displayed to the user. This allowsthe natural environment to be captured and redisplayed to the user withmixed reality components. Other AR headsets, such as Google Glass™ andMicrosoft HoloLens™ augment reality by providing supplementalinformation to a user that appear as holograms within the user's visualfield. Because the user views the environment either directly or througha clear lens, the additional displayed information, which is projectedoff of a reflective surface in front of the user's eyes or directly ontothe user's retina, appears semi-transparent to the user.

Commercially available HMDs typically include one or more outward facingcameras to collect information from the environment. These cameras caninclude visible light cameras and IR cameras. The HMDs may includeillumination sources that assist the cameras in collecting data from theenvironment. Most HMDs include some form of user interface that a usercan use to interact with the processor via the display of the HMD. Insome systems, the user interface can be a handheld remote control usedto select and engage displayed menus. This may not be ideal for asurgical environment due to sterilization issues and fluid-coveredhands. Other systems, such as the Microsoft HoloLens™ and Google Glass™,use one or more cameras to track gestures or MEMS accelerometers todetect motion by the user's head. A user can then use gestures tointeract with a virtual interface. For example, a virtual keyboard maybe holographically displayed, and a camera may track the user's fingermovements to allow the user to type on a virtual floating keyboard. SomeHMDs may also have a voice interface. In addition to the display, theheadset may provide haptic feedback through actuators or audio signalsto the user through an earpiece or speakers. Other onboard sensors of anHMD can include gyroscopes, magnetometers, laser or optical proximitysensors. In some embodiments, an HMD may also have a laser or otherprojecting device that allows information to be projected onto theenvironment, rather than holographically to the user.

While AR headsets can provide a more natural feel to a user than VR,because most of the image the user sees is natural, it can be difficultto properly superimpose and align displayed information with the user'sviewpoint. There have been many software initiatives in the industrythat address this issue, spearheaded by AR headset manufacturers.Therefore, AR headsets and VR headsets typically come with the softwaretools necessary to superimpose information into the user's visual fieldto align that information with what the user sees in the environment.

In some embodiments, information similar to that displayed on atraditional cart-mounted display is provided to the surgeon as part of arobotic assistive surgery system, such as the NAVIO surgical system. Insome embodiments, different HMDs worn by different people in thesurgical theatre can display different information at any time. Forexample, a surgeon can see information relating to what is in hiscurrent field of view, while an HMD worn by a surgical resident candisplay camera footage of what the attending surgeon sees and anyenhancements that the attending surgeon sees. In some embodiments, theresident may see additional patient information that may be helpful forthe resident to learn or to convey to the surgeon, such as pre-operativeimaging, patient files, information from the manufacturer of a tool ormedical device, etc.

HMDs include one or more cameras that capture the field-of-view (or awider or narrower version thereof) of the wearer and provide the imagesto a processor, thereby allowing the processor to resolve thetwo-dimensional image captured by the HMD and its relation to a 3D modelof the surgical theatre. For example, one or more tracking cameras ofthe HMD can capture the presence of fiducial marks of patient bonesand/or tools to determine how the wearer's perspective relates to a 3Dmodel of the patient and/or the tools. This can allow an image processorto extract features from the captured image, such as bone/tissue andtools, and use the information to display enhanced information over theimage being viewed by the wearer. For example, when a surgeon isoperating on a knee, a camera on the surgeon's HMD can capture what thesurgeon sees, allowing image processing software to determine where inthe three-dimensional space the surgeon is looking and to determinespecific patient features that the surgeon is looking at. Those specificpatient features can be located in the two-dimensional image by imageprocessing software using pattern matching or machine learningtechniques informed by the location of the surgeon's view in thesurgical scene.

For example, the surgeon may be looking at the tibial plateau andfemoral condyles. The camera on her HMD will capture this image in realtime (which includes practical processing and communication delays) andsend this image to image processing software. Image processing softwaremay identify the object being viewed as the tibial plateau and femoralcondyles (or may receive a hint based on the perspective) and look forpatterns in the image to identify the extent of these features withinthe surgeon's field of view. Information about the tibial plateau andfemoral condyles can then be overlaid with the image that the surgeonsees. Software may be able to accurately locate the two-dimensionalimage relative to a three-dimensional model of the surgical scene iffiducial marks are available in the image (or recent images) or thesurgeon's HMD includes fiducial marks that are captured by a roboticvision system or by the cameras of other HMDs in the room to allowcalculation of the pose of the surgeon's HMD cameras.

In some embodiments, information is overlaid in the user's visual fieldholographically. In some embodiments, information can be digitallyprojected from the HMD onto the environment. For example, a laser arrayMEMS mirror coupled to the headset can project an image directly onto asurface in the environment. Because this projection comes fromapproximately the same location as the user's eyes, this projection canbe easily collocated with the user's field-of-view, overlaying theinformation onto the environment in a more robust manner than presentinga floating hologram to the user. For example, as shown in FIG. 13, HMD1100 projects a computer-generated image of a cutting envelope onto aportion of a patient's knee 1102 to indicate to the wearer exactly whereon a bone in the knee she should cut without distracting the wearer orinterfering with her peripheral vision.

Surgical systems using an optical tracking modality, such as the NAVIOsystem, can be well-suited for use with HMDs. The one or more camerasincluded in an HMD make it especially convenient to adapt the HMD foruse in the surgical theatre. As shown in FIG. 14, one or more cameras1110 that are mounted to carts or fixed in the surgical environment useoptical and IR tracking to capture the location of fiducial markers 1112and 1114 mounted to tools and patient bones. Adding one or more HMDs1100 to this environment can supplement this tracking system byproviding additional perspectives for optical or IR tracking. In someembodiments, multiple HMDs 1100 can be used simultaneously in theoperating room, thereby providing a variety of perspectives to aid intracking the fiducial marks of tools and patient anatomy. In someembodiments, the use of multiple HMDs provides a more robust trackingmodality because additional perspectives can be compared, weighted,correlated, etc., in software to verify and refine a 3D model of theenvironment. In some embodiments, permanently mounted cameras in the OR(to a wall, cart, or surgical light) can utilize higher-quality IR andoptical components than those in the HMDs. Where there is a disparity inthe quality of components, 3D model refinement using multipleperspectives can assign heuristic weights to the different camerasources, allowing refinement with deference to higher-quality componentsor more reliable perspectives.

Given the rapid development of optical sensor technology spurred by themobile device market, HMD optical sensor technology is rapidly evolving.In some embodiments, a cart-mounted camera array is unnecessary for theoptical tracking modality. Optical and IR sensors on HMDs worn bysurgeons, residents, and nurses can provide sufficient perspectives fortracking fiducial marks on a patient and tools without the need for astandalone cart. This can mitigate the cost of adding HMDs toconventional tracking modalities or reduce the overall system cost. HMDprices for off-the-shelf components are rapidly declining as they becomeaccepted in the consumer market. In some embodiments, cart orwall-mounted cameras can be added to the system using lower qualityoptical and IR sensors than traditional cart-mounted tracking systems tosupplement embodiments that otherwise rely entirely on IR and opticalsensors of HMDs. (Optical sensors include IR sensors/cameras and opticalsensors/cameras, and may be described generically as cameras, but thesecomponents may be listed separately for clarity; embodiments may includeany subset of available optical sensors.)

As shown in FIG. 14, each camera array (of a cart-mounted trackingsystem 1110 and of each HMD 1100) has a camera pose that defines theframe of reference for the camera system. In the case of cart- orwall-mounted cameras, the camera pose may be fixed throughout theoperation. In the case of HMD cameras, the camera pose will often changeduring the operation as a user moves her head. Accordingly, someembodiments, where HMDs work along with cart- or wall-mounted trackingsystems to supplement the tracking information, use fiducial marks toidentify at least the location of the other camera systems in theenvironment. In some embodiments, fiducial marks can be rigidly appliedto these camera systems to enable other camera systems to identify thelocation and pose of each other camera system. This can be useful incalculating camera perspectives to determine a robust 3D model of thesurgical environment. It should be appreciated that as a user moves inHMD, the field-of-view may lose sight of other camera systems, butfiducial marks on the systems allow the camera system to quicklyidentify other cameras and their poses once they move back into thefield-of-view.

Once cameras has identified the location and/or the pose of othercameras in the environment, the cameras can identify the location andorientation of fiducial marks that are affixed to patient bones ortools. Where two cameras have the same fiducial markers in theirfield-of-view, a central processor or peer-to-peer processing cancorrelate the location and orientation of those marks relative to eachcamera to create a model of the 3D environment that includes thelocation and pose of each camera and the location and pose of eachoperable bone of a patient. In the example shown in FIG. 14, thefiducial markers 1112 and 1114 mounted to each patient bone include fourreflective points with known geometry. Each of these fiducial markers1112 and 1114 has a pose defined in three-dimensional space. Becausethis geometry is known, this pose can be correlated through aregistration process to define a frame of reference for each bone thatis a transfer function of the pose of the corresponding fiducial mark.Thus, when the pose can be determined by a processor from camerainformation, the pose of each bone 1116 and 1118 relative to eachcamera's field-of-view can be calculated. In this example, headset 1100and cart-mounted tracking systems 1110 communicate with one another orwith a central processor wirelessly.

The robustness of the 3D model is improved by the number of fiducialmarks that multiple cameras observe. Because cameras capture an analogworld with digital signals and are limited to the quality of opticalcomponents, the precision with which each camera can locate fiducialmarkers in space includes some degree of error. The use of multiplecamera systems or HMDs can reduce this error, thereby creating a morerobust 3D model with a level of precision unachievable with a singlecart mounted tracking modality. Any method of computing a 3D model of anenvironment from pose information captured by multiple cameras that isknown in the art may be adapted to a multi-HMD operating roomenvironment.

FIG. 15 shows an exemplary method 1200 for using AR headsets in thesurgical environment where fiducial marks are used on patients andtools. Each camera system in the operating room, which may includecameras on HMDs and fixed cameras, such as cameras mounted to carts orwalls, may perform steps 1202 through 1212. Each camera system may beinitialized at step 1202. This can include any startup procedures andcalibrations needed to prepare the camera for operation during surgery.At step 1204, the camera system captures images and applies anytechniques to prepare those images for processing, including removingdistortion or any image preprocessing. At step 1206, each camera systemcan optionally estimate its own pose. This can include gyroscopicsensors, accelerometer sensors, magnetic sensors, or compasses andmodels based on previous poses that are updated based on imageinformation. At step 1208, each camera system attempts to identify anyother camera systems that are in its field-of-view. This can be usefulfor determining the geometric relationships between various camerasystems. In some embodiments, this identification step looks for certainmarkers in the visual field that can include fiducial marks that areplaced on cameras or IR beacons or the like. At step 1210, each camerasystem identifies fiducial marks in the images captured. Fiducial marksmay be physical markers placed on or affixed to the patient's anatomy,tools, other cameras, or landmarks in the environment. The processorassociated with each camera system can determine pose information basedon these fiducial marks. This pose information can include the pose ofthat camera system and/or the pose of objects in the environment. Forexample, a tool may have a plurality of IR/UV-reflective or uniquelycolored or patterned markers placed on it such that the location andorientation of that tool can be identified from one or more images.Similarly, a camera may have a plurality of fiducial markers placed uponit to allow the camera's position and orientation to be calculated byother camera systems. This information about the position andorientation of other objects in the environment may be reportedwirelessly or through a hard wired network to a central processor thatmanages the system, at step 1212. The above-listed steps (1204-1212) maybe repeated as each camera system continuously captures images andprepares them for analysis.

A central processor receives the image data and the pose or fiducialmark information from each camera system at step 1214. At step 1216, thecentral processor creates or updates a model of all camera systems inthe environment and their poses relative to an environment referenceframe. At step 1218, the central processor identifies fiducial marks inthe received images to correlate marks captured by multiple camerasystems. By capturing such marks in multiple fields of view, differentperspectives of an object can be used to refine the determination of theposition and orientation of the object in the environment. Oncecorrelated, at step 1220, the central processor can calculate theposition and orientation of each object having fiducial marks, such astools, environmental landmarks, other HMDs or camera systems, andpatient anatomy. At step 1222, this information is used to update a 3Dmodel of the environment, calculating a position and orientation ofobjects relative to a fixed reference frame, as well as identifying thepose of reference frames defined by each object. For example, apatient's femur has a given pose based on the way the patient is layingwithin the operating room. That femur also has its own reference frame,which is useful when correlating preoperative imaging with the structureof the patient's femur and identifying the portions of that femur thatmay need to be resected during surgery. At step 1224, the centralprocessor can use the updated 3D model to send information about theenvironment to a surgical robotic system and any HMDs in the operatingroom. The central processor continues to receive images and updatemodels of the environment. In some embodiments, this process can beenhanced via other sensors, such as accelerometers, compasses, etc., forany objects within the operating room. Once this 3D model is generated,the central processor is free to interact with individual HMDs inaccordance with numerous software applications described herein.

FIG. 16 is a system diagram of an augmented reality system 1300 for useduring surgery. In this example, fiducial markers 1302A-G are placed oneach HMD and camera system, as well as a patient's femur and tibia. Inthis example, a partial or total knee replacement is being performed. Acart-mounted camera system 1110 (such as that available with the NAVIOsurgical system) is placed with a view of the surgical scene. Fiducialmarkers 1302E-F having a plurality of fiducial marks (such as three ormore spherical IR reflective marks) are temporarily, mechanicallyaffixed to the patient's femur and tibia. This allows cart-mountedcamera system 1110 to track the pose and motion of the tibia and femur.This can be used to determine the pivot center between these bones, aswell as other information about the patient's anatomy during motion. Asurgical robot 1306 may also use this information to determine the idealplacement of cuts and replacement knee parts. The cart-mounted camerasystem 1110 communicates via a local area network (e.g., a secure Wi-Finetwork) with a central processor 1304 that includes software and memorysufficient to process the images from the cart-mounted camera todetermine an environmental model and to calculate any information thatmay be useful to the surgical robot 1306 to assist the surgeon in theoperation. In some embodiments, central processor 1304 is part of thecart that supports the camera system. In these embodiments, cart-mountedcamera system 1110 may communicate directly with central processor 1304.Central processor 1304 then communicates with any robotic systems 1306that are used during the procedure.

In addition to this traditional robotic surgery system, a plurality ofHMDs 1100, 1100A, and 1100B are worn by doctors and house staff duringthe procedure. Each HMD has an identifier that allows that HMD to beidentified in the visual plane of the cart-mounted camera system 1110.In some embodiments, the identifier includes an IR emitter that sends abinary modulated code identifying the specific HMD or wearer.

In this embodiment, each HMD 1100-1100B has an array of cameras (IRand/or visible spectrum) that capture the visual field of the user (orsubsection or wider angle, thereof) from multiple positions, creating astereoscopic (or higher order) view of the scene for image processing.This allows each headset to capture image data sufficient to providethree-dimensional information about the scene. Each HMD captures adifferent prospective of the surgical scene. As explained in FIG. 15,the camera arrays of each HMD capture images of the scene, estimates thecurrent pose of the HMD (which can be by non-optical sensors as well),and identifies other cameras in the field-of-view and any fiducialmarks. In some embodiments, HMDs have an IR emitter to illuminatereflective fiducial marks. These captured images can then be processedonboard the HMD or sent to the central processor for more powerful imageprocessing. In some embodiments, a preprocessing step is performed oneach HMD to identify fiducial marks and other cameras and to estimateposes for fiducial markers and other cameras. Each HMD can communicatewith other HMDs or with the central processor 1304 over local network1310, such as a secure Wi-Fi network. The central processor 1304receives the image information from each HMD and further refines the 3Dmodel of the surgical space.

Based on the images captured from each HMD, central processor 1304 candetermine which objects in the three-dimensional model of the surgicaltheatre correspond to reference features in the two-dimensional imageplane of each HMD. This information can then be communicated back to theHMD over the network 1310. In some embodiments, each HMD receivesinformation about the three-dimensional model at regular intervals, butuses onboard image processing to track objects based on thethree-dimensional model information received from the central processor.This allows the headset to accurately track objects within thefield-of-view in real time without delays caused by networkcommunication.

In some embodiments, an HMD 1100 receives information about objects fromthe central processor 1304 and processes current and recent images toidentify the salient features of an object corresponding to thethree-dimensional model information received from the central processor.The HMD 1100 uses local image processing to track those features (andthe associated objects) in the visual plane. This allows the HMD 1100 tohave reference pixel locations to overlay information related toobjects.

For example, a surgeon's HMD 1100 can capture fiducial marks 1302E and1302F relating to the tibia and femur. Based on communication with thecentral processor 1304 to identify a three-dimensional model of thetibia and femur, the surgeon's HMD 1100 can identify which features inthe two-dimensional images correspond to features of the tibial plateauand femoral condyles. By tracking the fiducial marks on these bones orby tracking the image features identified as the tibial plateau orfemoral condyles, the surgeon's HMD 1100 can track these features of thebones in real time (taking into account any processing and memorydelays) without worrying about network lag. If software running on HMD1100 wishes to overlay visual indicators of where to cut on the tibialplateau, the HMD can track the tibial plateau as the surgeon's headmoves or the patient's leg moves. The surgeon's HMD 1100 can accuratelyestimate where the reference points of that bone feature are and whereto display the augmented holographic feature.

In some embodiments, HMDs 1100-1100B can communicate directly to assistone another in updating any positional models. Using peer-to-peercommunication, HMDs 1100-1100B can present their models of theenvironment to other HMDs allowing them to rectify their own modelsusing arbitration rules.

In some embodiments, objects within the surgical space can include QRcodes or other visual indicators that can be captured by a camera on aHMD 1100 to convey information about that object to the HMD directly.For example a surgical tray 1308 having tools for a given surgery canhave a QR code indicating the identity of the tray. By consulting adatabase, the HMD 1100 capturing a QR code can identify exactly whichobjects and surgical tools are on that tray prior to the start of thesurgery. This can allow the HMD 1100 to identify important objectsrelating to the surgery, such as various knives, cutting tools, sponges,or implantable devices. Then, the HMD 1100 can track these objectswithin the surgical space taking into account the last known location ofthose objects. In some embodiments, HMDs 1100-1100B can shareinformation about the last known location of objects in the surgicaltheatre to make it faster or easier for each headset to identify objectsthat come into the field of view of each HMD. This can assist thesurgeon in quickly identifying which tool or object to grab for the nextstep of the surgery. For example, a tray may include a series of cuttingguides that are used in various surgeries. However, each patient mayonly need a single cutting guide used on his femur. HMD 1100 canidentify the cutting guides in the tray based on the tray's QR code andan initial layout and track the individual cutting guides. Theholographic display of the HMD 1100 can superimpose an indicator to thesurgeon of which cutting guide to use for a given step in the procedure.

In different embodiments, various information can be displayed to a userof an HMD. For example, in FIG. 17A, a user is presented with aholographic superimposition 1315 of a resection area on a femoralcondyle to indicate exactly where a surgeon should remove tissue forseating a replacement knee part that will be cemented and affixed tothat femoral head. FIG. 17A is the view that the surgeon sees, includingthe natural scene and a superimposed shape that is placedholographically onto the surface of the bone. In some embodiments,rather than using a hologram as part of a conventional AR display, alaser or projector mounted to the HMD can project this informationdirectly onto the surface of the bone. Cameras on the HMD can provide afeedback loop to ensure proper and consistent placement of theprojection onto the bone.

In FIG. 17B, an exemplary display of the three-dimensional model isshown. Conventionally, this display might appear on a computer screenfor robotic surgery system. It indicates on the three-dimensional modelexactly where the surgeon's tool should go and where the tool should cutthe bone. This information can be adapted for an AR display, overlayinga part of the three-dimensional model of the bone holographically ontothe bone, allowing the resection area to be displayed to the user wholooks at the real-world bone. In some embodiments, only thethree-dimensional model of the resection area is displayed, while athree-dimensional model of the bone is compared to the visual field ofthe HMD to ensure proper placement of the holographically displayedthree-dimensional model of the resection area. In addition, any of themenus or additional information shown in FIG. 17B can be displayed as aholographic menu to the user of the HMD, allowing the user to selectcertain user interface menus for additional information or views throughthe standard AR user interface.

FIG. 18A shows an additional three-dimensional model view that can bedisplayed to a user. FIG. 18A depicts points identified by a point probein a femoral head mapping process. Such information can be displayed toa surgeon via a holographic image of the portions of bone that have beenprobed to identify the shape of the femoral head. In some embodiments,this information may be displayed on a resident's HMD. The image shownin FIG. 18A could be displayed on a conventional computer display withinthe operating room. Individual portions of a display, such as thethree-dimensional model of the femoral head can be displayedholographically onto the bone using the AR display. Again, any of thesemenus shown can be displayed to the user's headset or can be displayedon the two-dimensional LCD-type display on a cart in the room. Thesemenus can be selected by a user of an HMD to change views or getadditional information. The user of an AR HMD can make a selection ofthe menu using any conventional AR selection means, such as air clicksor head/hand gestures that can be detected by sensors or cameras in theHMD. In some embodiments, where robotic cutting tools are used, thisdisplay may indicate where a robot is to cut, allowing visualconfirmation by the surgeon before cutting begins.

FIG. 18B shows another three-dimensional model of the femur that showsproper placement of the femoral cutting guide. This can be displayed toa surgeon holographically to indicate exactly where the femoral cuttingguide should be placed. During alignment of the cutting guide, a surgeoncan consult this holographic superimposition to ensure that theplacement is approximately correct. Robotic vision systems within theroom can provide a final confirmation before mounting the cut guide. Ina hip procedure, this same technology could be used to aid the surgeonwith placing a custom femoral neck cut guide, or an acetabular jig usedto help with cup placement. In some embodiments, the display in FIG. 18Bcan be selected from menus in FIG. 18A, allowing a surgeon to switchbetween a history of steps and a model of the next steps that can beaccomplished. This can allow a user to effectively rewind andfast-forward the procedure, seeing steps to be performed in the futureand the steps that have already been performed.

In some embodiments, a user may also select and modify proposed cuttingangles, allowing a processor to calculate using a model of anatomy, suchas LIFEMOD™ by SMITH AND NEPHEW, INC., how changes in the cutting anglesmay affect the geometry of the replacement knee. Displayed informationcan include static and dynamic forces that will occur to ligaments andtissue of a patient if cutting geometry is altered. This can allowsurgeon to modify the replacement knee procedure on the fly to ensureproper placement of replacement knee parts during the procedure tooptimize the patient's outcome.

FIG. 19 shows a three-dimensional model of a complete replacement kneesystem including a model of ligament and tendon forces. A portion ofthis model can be displayed holographically to a surgeon duringoperation if the surgeon wishes to see how cutting decisions during theprocedure can affect hinge geometry and resulting stresses on ligamentsand tendons. Exemplary modeling of the placement of an implant and ofother parameters for an arthroplasty procedure is described in U.S.patent application Ser. No. 13/814,531, which was previouslyincorporated herein by reference. A surgeon can change parameters of thearthroplasty via the AR interface to create hypothetical results thatmay be displayed via the headset, such as whether a parameter changeduring a total knee arthroplasty (TKA) cause a patient's gait to becomemore varus or valgus or how a patella will track. Optimized parameterscan also be modeled and displayed to the surgeon the procedureholographically.

In some embodiments, the wearer of an HMD can selectively requestdisplay of patient history information including preoperative scans ofpatient tissue. In some embodiments, the display of the scans can beholographically overlaid to the existing patient tissue observed in thescene by aligning features from the imaging to features found in thepatient. This can be useful for guiding a surgeon to determine theproper resection area during a procedure.

In some embodiments, video of a three-dimensional model of data can berecorded, logged, and time stamped. Playback of the video can beperformed after the procedure or the video could be called up on an HMDdisplay during the procedure to review one or more steps. This may be avaluable teaching tool for residents or for a surgeon wishing to seewhen a certain cut or step was undertaken. Playback may be a useful toolto create a change of surgical plan during the procedure based on eventsduring the procedure. Head or hand gestures by an operator of the HMDcan rewind or advance the virtual viewing of video or 3-D modelinformation.

Exemplary Use Cases

By adding AR to an operating theater using HMDs, many improvements tothe operative flow in various surgeries can be achieved. The followingare some examples of the ways in which AR can be utilized in varioussurgical procedures.

Software can utilize the HMD camera and display to determine (with theassistance of preoperative plan and processor) the ideal startinglocation for an incision. Through holographic overlay (or by projectingdirectly onto patient skin), the line defining the extents of anincision location can be displayed to a surgeon looking at the patient.The exact location can consider the specific patient anatomy andintraoperative point registration, where a user registers patientgeometry with the system more accurately.

Soft tissue dissection can utilize built-in cameras of the HMD and themodel of the patient to highlight certain muscle groups, ligaments of ajoint, such as a hip capsule or knee, nerves, vascular structures, orother soft tissues to aid the surgeon during dissection to get to thehip joint or knee. The augmented display can display indicators to asurgeon of the location of these soft tissues, such as byholographically displaying a 3D model or preoperative imaging.

During a hip replacement or repair, software may superimpose a lineacross the proximal femur that indicates the ideal neck cut based on thepre-operative plan. This can define a precise location for cutting thebone to place a replacement femoral head prosthesis. Similar to theabove examples, during acetabular reaming, the heads-up display can showthe amount of bone that needs to be removed by overlaying differentcolors onto the patient's acetabulum. This allows the surgeon to knowwhen he/she is getting close to the floor of the acetabulum. In someembodiments, the extent of the resection area used for reaming theacetabulum can be superimposed onto the patient bone. A color indicator,such as green, yellow, and red, can indicate to a surgeon (based on thelocation of his/her tool) how deep the reaming has gone relative to thepredetermined resection area. This provides a simple indicator duringsurgery to avoid removing too much bone. In some embodiments, theheads-up display may superimpose an image of a reamer handle (or othertool) to indicate the proper inclination and anteversion from thepre-operative plan (or from surgeon input). It may also display theactual inclination/anteversion of the reamer/tool handle to allow thesurgeon to correct their approach angle.

During a cup impaction step for hip replacement, similar to reaming, theinclination and anteversion values can be displayed on the heads-upunit, along with the values that were determined from the pre-operativeplan. A superimposed image of a cup impactor (or a long axis) may bedisplayed to aid the surgeon with positioning the implant. The HMD canalso display an indication of how far away the cup is from being fullyseated. For example, a measurement value or a change of a color of asuperimposed cup or impactor can be used to indicate whether the cup isfully seated or overlaying a model of the final ideal location of thecup that highlights the difference from what the surgeon currently sees.

An HMD can highlight screw holes that should be used to affix anyhardware to a bone. While using a drill and a drill guide, the heads updisplay may superimpose an “ideal” axis for screw insertion to aid withpositioning of the screw within the screw hole.

During femoral canal preparation, an HMD can superimpose an image of abroach in the proper orientation (corresponding to the pre-operativeplan) while the surgeon is inserting the broach into the femoral canal.Additionally, there can be an indication superimposed onto the scene togive the surgeon information related to final broach seating. This canbe shown by a changing of color around the broach or by a percentage ornumber that indicates to the surgeon if the broach is fully seated andwhether or not that size is the correct “final” component size.

A surgeon performing a trial reduction can be given the option todisplay a combination of leg length and offset increases based on theimplant sets being used (e.g., in chart form). For example, the chartcan list a combination of leg length and offset changes for each of theimplant combinations. Alternatively, there may be an option to have theproposed/changed components superimposed on the patient's anatomy toshow the surgeon what the resultant implant positioning would be if theywere to change the neck offset/femoral head length (e.g., changing fromSTD to High offset neck, +0 to +4 femoral head). The surgeon can selectthe appropriate implants from a first trial and perform the implantationstep. Multiple trialing steps, a conventional standard of care, wouldlikely be unnecessary.

Resurfacing techniques can also be improved through AR. When performinga resurfacing procedure, the HMD can superimpose an axis that indicatesthe ideal position and orientation of a guide wire. In some embodiments,software allows the surgeon to adjust this axis, and the HMD cansuperimpose a cross-sectional or other view of the femoral neck todisplay to the surgeon how thick the bone would be if the implant wereinserted at the position (one of the most common complications fromresurfacing surgery is inserting the component in varus, which can leadto femoral neck fracture). Giving the surgeon the ability to adjust thisaxis may enable optimization of the performance of the implants in-vivo.

In some embodiments, this traditional femoral resurfacing technique canbe replaced by a burr-only technique. In this exemplary technique, asurgeon prepares the proximal femur entirely by burring. The bone mapcan be superimposed onto the bone to indicate how much bone is left tobe removed. This can also be indicated by color on the map. A variety ofcutting instruments can be made available to the surgeon to reduce theoverall amount of time required to cut the bone to the desired shape.

Any suitable tracking system can be used for tracking surgical objectsand patient anatomy in the surgical theatre. For example, a combinationof IR and visible light cameras can be used in an array. Variousillumination sources, such as an IR LED light source, can illuminate thescene allowing three-dimensional imaging to occur. In some embodiments,this can include stereoscopic, a tri-scopic, quad-scopic, etc., imaging.In addition to the camera array, which in some embodiments is affixed toa cart, additional cameras can be placed throughout the surgicaltheatre. For example, handheld tools or headsets worn byoperators/surgeons can include imaging capability that can communicateimages back to a central processor to correlate those images with imagescaptured by the camera array. This can give a more robust image of theenvironment for modeling using multiple perspectives. Furthermore, someimaging devices may be of suitable resolution or have a suitableperspective on the scene to pick up information stored in QR codes orbarcodes. This can be helpful in identifying specific objects notmanually registered with the system.

In some embodiments, specific objects can be manually registered by asurgeon with the system preoperatively or during operation. For example,by interacting with a user interface, a surgeon may identify thestarting location for a tool or a bone structure. By tracking fiducialmarks associated with that tool or bone structure, or by using otherconventional image tracking modalities, a processor may track that toolor bone as it moves through the environment in a three-dimensionalmodel.

In some embodiments, certain markers, such as fiducial marks thatidentify individuals, important tools, or bones in the theater mayinclude passive or active identifiers that can be picked up by a cameraor camera array associated with the tracking system. For example, an IRLED can flash a pattern that conveys a unique identifier to the sourceof that pattern, providing a dynamic identification mark. Similarly, oneor two dimensional optical codes (barcode, QR code, etc.) can be affixedto objects in the theater to provide passive identification that canoccur based on image analysis. If these codes are placed asymmetricallyon an object, they can also be used to determine orientation of anobject bay comparing the location of the identifier with the extents ofan object in an image. For example, a QR code may be placed in a cornerof a tool tray, allowing the orientation and identity of that tray to betracked. Other tracking modalities are explained throughout. Forexample, in some embodiments, augmented reality headsets can be worn bysurgeons and other staff, providing additional camera angles andtracking capabilities.

In addition to optical tracking, certain features of objects can betracked by registering physical properties of that object andassociating them with objects that can be tracked, such as fiducialmarks fixed to a tool or bone. For example, a surgeon may perform amanual registration process whereby a tracked tool and a tracked bonecan be manipulated relative to one another. By impinging the tip of thetool against the surface of the bone, a three-dimensional surface can bemapped for that bone that is associated with a position and orientationrelative to the frame of reference of that fiducial mark. By opticallytracking the position and orientation (pose) of the fiducial markassociated with that bone, a model of that surface can be tracked withan environment through extrapolation.

FIG. 20 provides an example of a parallel processing platform 2000 thatmay be utilized to implement the Surgical Computer 150, the SurgicalData Server 180, or other computing systems used in accordance with thepresent invention. This platform 2000 may be, for example, used inembodiments in which machine learning and other processing-intensiveoperations benefit from parallelization of processing tasks. Thisplatform 2000 may be implemented, for example, with NVIDIA CUDA™ or asimilar parallel computing platform. The architecture includes a hostcomputing unit (“host”) 2005 and a graphics processing unit (GPU) device(“device”) 2010 connected via a bus 2015 (e.g., a PCIe bus). The host2005 includes the central processing unit, or “CPU” (not shown in FIG.20), and host memory 2025 accessible to the CPU. The device 2010includes the graphics processing unit (GPU) and its associated memory2020, referred to herein as device memory. The device memory 2020 mayinclude various types of memory, each optimized for different memoryusages. For example, in some embodiments, the device memory includesglobal memory, constant memory, and texture memory.

Parallel portions of a big data platform and/or big simulation platformmay be executed on the platform 2000 as “device kernels” or simply“kernels.” A kernel comprises parameterized code configured to perform aparticular function. The parallel computing platform is configured toexecute these kernels in an optimal manner across the platform 2000based on parameters, settings, and other selections provided by theuser. Additionally, in some embodiments, the parallel computing platformmay include additional functionality to allow for automatic processingof kernels in an optimal manner with minimal input provided by the user.

The processing required for each kernel is performed by a grid of threadblocks (described in greater detail below). Using concurrent kernelexecution, streams, and synchronization with lightweight events, theplatform 2000 (or similar architectures) may be used to parallelizeportions of the machine learning-based operations performed in trainingor utilizing the smart editing processes discussed herein. For example,the parallel processing platform 2000 may be used to execute multipleinstances of a machine learning model in parallel.

The device 2010 includes one or more thread blocks 2030 which representthe computation unit of the device 2010. The term thread block refers toa group of threads that can cooperate via shared memory and synchronizetheir execution to coordinate memory accesses. For example, threads2040, 2045 and 2050 operate in thread block 2030 and access sharedmemory 2035. Depending on the parallel computing platform used, threadblocks may be organized in a grid structure. A computation or series ofcomputations may then be mapped onto this grid. For example, inembodiments utilizing CUDA, computations may be mapped on one-, two-, orthree-dimensional grids. Each grid contains multiple thread blocks, andeach thread block contains multiple threads. For example, the threadblock 2030 may be organized in a two dimensional grid structure with m+1rows and n+1 columns. Generally, threads in different thread blocks ofthe same grid cannot communicate or synchronize with each other.However, thread blocks in the same grid can run on the samemultiprocessor within the GPU at the same time. The number of threads ineach thread block may be limited by hardware or software constraints.

Continuing with reference to FIG. 20, registers 2055, 2060, and 2065represent the fast memory available to thread block 2030. Each registeris only accessible by a single thread. Thus, for example, register 2055may only be accessed by thread 2040. Conversely, shared memory isallocated per thread block, so all threads in the block have access tothe same shared memory. Thus, shared memory 2035 is designed to beaccessed, in parallel, by each thread 2040, 2045, and 2050 in threadblock 2030. Threads can access data in shared memory 2035 loaded fromdevice memory 2020 by other threads within the same thread block (e.g.,thread block 2030). The device memory 2020 is accessed by all blocks ofthe grid and may be implemented using, for example, DynamicRandom-Access Memory (DRAM).

Each thread can have one or more levels of memory access. For example,in platform 2000, each thread may have three levels of memory access.First, each thread 2040, 2045, 2050, can read and write to itscorresponding register 2055, 2060, and 2065. Registers provide thefastest memory access to threads because there are no synchronizationissues and the register is generally located close to a multiprocessorexecuting the thread. Second, each thread 2040, 2045, 2050 in threadblock 2030, may read and write data to the shared memory 2035corresponding to that block 2030. Generally, the time required for athread to access shared memory exceeds that of register access due tothe need to synchronize access among all the threads in the threadblock. However, like the registers in the thread block, the sharedmemory is typically located close to the multiprocessor executing thethreads. The third level of memory access allows all threads on thedevice 2010 to read and/or write to the device memory 2020. Devicememory requires the longest time to access because access must besynchronized across the thread blocks operating on the device.

The embodiments of the present disclosure may be implemented with anycombination of hardware and software. For example, aside from theparallel processing architecture presented in FIG. 20, standardcomputing platforms (e.g., servers, desktop computer, etc.) may bespecially configured to perform the techniques discussed herein. Inaddition, the embodiments of the present disclosure may be included inan article of manufacture (e.g., one or more computer program products)having, for example, computer-readable, non-transitory media. The mediamay have embodied therein computer readable program code for providingand facilitating the mechanisms of the embodiments of the presentdisclosure. The article of manufacture can be included as part of acomputer system or sold separately.

Applying Statistical Models to Optimize Pre-Operative or Intra-OperativePlanning by Patient Activity

There is a need for simple and processor efficient planning tools forpatient-specific preoperative or intraoperative planning by surgicalstaff. The preoperative planning stage for arthroplasty should becomputationally and labor efficient due to the volume of surgeries andlimitations on the time of engineers and surgeons, while theintraoperative planning stage places more computational limitations onany simulation data because there is no time to wait for simulations inthe surgical theater. With the rise of cheap tablet or mobile devices(lower powered computational systems in general, such as cart workstations in a surgical theater) there is an opportunity to provide lowprocessor overhead applications that can assist surgical staff ingathering data and planning surgical procedures that utilizes morepowerful systems that are interfaced across a network that maintain datastores of simulation or real-world data from past patient cases. Thesebackend systems can maintain, manipulate, create, and mine large amountsof data, allowing lower powered devices in a surgical office or to takeadvantage of this trove of information. An ideal planning applicationshould assist surgical staff in gathering data and planning surgicalprocedures and access data stores of simulation or real-world data frompast patient cases.

For example, in some embodiments, in-theater or mobile devices with anintuitive touchscreen interface, wireless networking ability, andcameras lend themselves particularly well to assisting creation ormodification of a surgical plan that can be used with a CASS, either ina pre-operative phase or during an operation. Their interface can behelpful gathering and interacting with new information (e.g., imaging orforce characteristics of a joint captured during surgery) that can beused to improve a surgical plan. Devices such as tablets, laptops, orcart-based computers often lack powerful processors, which can beimproved by utilizing a server or a database of past simulation orclinical results to give devices in a surgical theater or office theadvantages of patient-specific simulation without the need to runsimulations locally or on-demand. Some embodiments of the surgicalplanning tool utilize a network interface to allow remote databases orserver processors to offload some of the data storage or processing fromthe in-theater or mobile device. By looking up or learning from past,similar simulations, these databases provide an opportunity forefficient, on-the-fly estimations of simulation results for givenpatient data that lend themselves particularly well to low processoroverhead applications.

Some embodiments recognize that patient goals for a surgery are oftenunique and personal. One patient may want simply to get back to apain-free, rather sedentary life, while another might hope to get backto an active life of golf and biking or running. By utilizing a largestore of data of similar patient simulations or past results,preoperative data can assist a surgeon in optimizing a surgical plantoward these specific activity-based goals. Each of a variety of commonactivities (e.g., walking, stairclimbing, squatting, bending over, golf,etc.) can be characterized by a motion profile that accounts for theactual motion that a joint will undergo during an exemplary repetitivemotion associated with that activity and the expected stresses on animplant and soft tissue. Simulations of the repetitive motion profilefor each activity can be done for a variety of patient joint geometriesto populate the database of simulation results. The CASS or anapplication on a user's computing device can then solicit selection ofthe activities for which the patient surgical plan is optimized. Thesurgical plan can then focus on the simulation results relevant to thosemotion profiles while ignoring or weighting less heavily simulationresults that are relevant to unselected activities.

An exemplary embodiment of a surgical application or CASS that utilizespast simulation results is a knee replacement planning tool. It shouldbe noted that these techniques can also be applied to other resectionsurgeries, such as partial knee replacement, hip replacement, shoulderreplacement, ankle replacement, spinal resection, etc., or any procedurewhere patient geometry impacts performance of the prosthetic implant. Inthe example of the knee replacement planning tool, the planning tool canassist the surgeon in selecting the proper size of the implants, theproper position and orientation, and the type of implants to be usedwhen implanting the prosthesis to maximize mobility and minimize thechance of failure due to premature wear, impingement, dislocation, orunnecessary loading of the implant or ligaments during expectedactivity.

In the context of THA, existing guidelines (e.g., those that use theLewinnek “safe zone”) that utilize a rule of thumb ranges for abductionand anteversion angles for acetabular cup placement may not be enough tominimize the risk of hip dislocation once the patient has recovered. Forexample, recent studies have shown that more than 50% of postoperativedislocations occur in implants that were installed within the Lewinnek“safe zone.” Studies have also shown that spinal pelvic mobility of thepatient may directly influence proper acetabular cup placement, whichmay not be considered in traditional guidelines. Accordingly, properimplant position and orientation can benefit from a planning tool thatutilizes simulation results to account for patient specific riskfactors.

An embodiment of a surgical planning tool can be an application thatruns on a desktop, server, laptop, a tablet computer, mobile phone, orcart-based workstations. The exemplary applications consider primarilygeometry data within x-rays (or other medical image data such as CT,Mill, ultrasound, etc.) which can minimize the impact of x-ray/imagedistortion. In some embodiments, image processing software can estimatethe location of salient points and distances within the image, or atouchscreen (or other) interface may allow a user to easily manipulatethese points and measurements by dragging them around on the image untilthe surgeon is satisfied with the accuracy of placement relative topatient anatomy. For example, in PKA/TKA, anatomical axes for femur andtibia can be extracted from an image to determine varus/valgus angle;center points and radii for the distal and posterior portions of themedial and lateral condyles can be extracted; medial and lateral gapsbetween the tibial plateau and respective condyles can be measured fromimages at various degrees of flexion. The shape of the patellar groovecan be determined from anterior/posterior images of various degrees offlexion, while depth can be determined from lateral images or MRI. Asurgeon can also estimate tension or laxity of ligaments by applyingforces to the knee at predetermined degrees of flexion to supplementimage data. Some embodiments use a combination of an estimate of thelocation of these points (which may be learned from past interactionswith surgeon users, as they place these points on the image) donethrough automatic image processing (guided by searching for salientfeatures as learned by a training set of past human selections) andrefinement by a user using the touchscreen or computer. In someembodiments, images of an extended and a flexed knee (e.g., lateral andAP x-ray or an MRI) in two or more positions are considered. In someembodiments, an x-ray (or other image) of the patient standing and anx-ray (or other image, such as an MRI) of them sitting are considered.This can give the system an estimate of the change in pelvic tiltbetween standing and sitting, which can be utilized in estimatingpatient mobility issues. In other embodiments, an x-ray of the patientin a challenging position, such as a flex-seated position orhyper-extension while standing, may be considered. Some embodiments alsoutilize motion capture systems to provide information regarding existingjoint mobility of the patient, such as pelvic mobility limitations.

Once images are landmarked to identify geometric features andrelationships of patient anatomy (automatically through image analysisor manually through touchscreen/UI manipulation), the simulation modelcan also include any additional patient conditions, such as spinal orhip mobility concerns. (For example, in the case of THA, conditions mayinclude a specific range of motion in the sagittal plane as well as ameasure of stiffness. This can be important for the positioning of animplant device in a patient to reduce the incidences of edge loading anddislocation for the patient's expected activity level.) The planningapplication can then perform a lookup of previously performed anatomicalsimulation results or perform a calculation based on a transfer functionextracted from a multitude of past anatomical simulation results (ofvarious patient geometries and attributes) to create a profile of theligament impingement risks, ligament stresses, and condylar compartmentgaps, and patellar tracking (for PKA/TKA) or center of pressure andrange of motion between the femoral head and the acetabular cup (THA)throughout the range of motion during the repetitive motion profile foreach of various selected activities. In some embodiments, variousactivities to consider can be guided by the patient's lifestyle andactivity level and the aggregate results of the simulations for eachactivity. The user can then be presented with a simple user interfaceoption to change position and orientation of the distal and posteriorcuts, and patellar attachment points/stuffing (for PKA/TKA) or abductionand anteversion angles (THA) to see how implant position and orientationaffects these characteristics of the resulting joint. The user may alsobe presented with different implant options that affect the performanceof the resulting joint, such as target gap size, artificial cartilagethickness, implant size and model, tibial and femoral implant depth,laxity, etc. For THA, these may include, but are not limited to: femoralhead diameter, liner type (neutral, hooded, anteverted, ceramic,constrained, dual mobility, or resurfacing), standard/high offset,femoral head offset, implant style/family (i.e., flat tapered wedge vsfit & fill), implant depth in bone, implant size, etc. Because theresults can be generated quickly through table look up oralgorithmically using a transfer function from past simulation results,the user gets seemingly instantaneous feedback in the interface aboutthe effects of his or her choices in implant type andposition/orientation. This can be especially helpful in the surgicaltheater when new information about a patient is gathered (such as forceloading of a joint, soft tissue laxity, etc.) or when a surgeon requestschanges to an existing preoperative plan, and provides rapid feedback onhow the changes or new information affects expected outcome and jointperformance.

In some embodiments, a user is also presented with a button that allowsthe system to automatically suggest optimized distal and posterior cutplanes, tibial implant depth, patellar pose and stuffing, as well asimplant type and size to minimize deviation in tibial-condyle gaps, andligament tension and laxity throughout a range of activities, while alsominimizing the amount of ligament release needed during surgery toachieve acceptable balance (for TKA/PKA). The results of these choicesor optimization can then be easily added to the surgical plan of theCASS or shared between colleagues with at-a-glance information aboutrange of motion and center of pressure. The optimization can occur byusing any suitable algorithm, such as by incrementally adjusting theangles or implant types and re-running the database search or transferfunction calculation until a local or global maximum is achieved. Insome embodiments, the optimization occurs beforehand for each possiblecombination of patient geometries (or within each combination in areasonable range), searching for the distal and posterior cut poses orimplant types for that combination that minimizes the deviation in thecompartment gaps or strain/laxity of ligaments (PKA/TKA) or theanteversion and abduction angles or implant types for that combinationthat minimizes the risk of edge loading or dislocation (for THA).

In some embodiments, all reasonable combinations or a subset of allcombinations of patient geometry are simulated to create a simulationdatabase to be used for subsequent real-world patient implantationplans. Once a sufficiently large number of simulations are performed, analgorithm can search for a transfer function that approximates theresults of these simulations, allowing interpolation of simulation mapsfor different combinations of implant types and positions/orientationswithout actively simulating every combination. Because each anatomicalsimulation can take several minutes to perform, determining a transferfunction from all or a subset of such past simulations can speed theprocess of determining a plan for a particular patient. In someembodiments, a subset of implant orientations can be simulated and theimplantation angles optimized to populate a results database. A suitablemachine learning algorithm can then be applied to create a learnedresults model that can be applied to estimate results for additionalcombinations of parameters. This allows dynamic creation of a goodestimate of flexion and extension gaps and ligament tension (PKA/TKA) oredge loading or dislocation stresses (THA) for various activities andimplant orientations by the results model without fully simulating themotion using an anatomical model (which is processor intensive eachtime) for every combination. Exemplary algorithms that can be appliedalone or in combination to develop and train a learned results model orto optimize selection of angles include, for example, linear orlogistics regression, neural networks, decision tree, random forest,K-means clustering, K nearest neighbor, or suitable deep learning opensource libraries. The transfer function can be identified throughregression analysis, neural net creation, or any suitable AI/analysisalgorithm that can estimate the parameters of the transfer function.

By running many different simulations for different implant orientationsand types for each activity and simulating loads, stresses, and rangesof motion in a joint, the database or model created from analyzing theseresults can act as a statistical model that describes the output of amulti-body system for a given combination of outputs for each activity.This can be accomplished through simulating all possible inputs formodels with only a few degrees of freedom, as a transfer function forfitting a multi-dimension expression that closely estimates the responseof the system after mining hundreds or thousands of simulations of asubset of possible parametric combinations, or by applying conventionalmachine-learning software systems to create an AI-driven model thatapproximates the system response of a multi-body model of the joint,based on a large number of simulations. Any of these statistical modelscan be used in different embodiments to model joint behavior in a waythat approaches the accuracy of an on-demand simulation of apatient-specific model for a given activity without the performanceimpracticalities of on-demand simulation of a detailed multi-body modelof a patient joint.

These same concepts can be applied to the selection of position andorientation for other implantable devices, such as total or partial kneereplacement, allowing user to easily markup x-ray/image data tomanipulate implantation position and orientation of the hardware and begiven fast, at-a-glance feedback on how those positions and orientationsaffects risk factors associated with the implant based on a patient'sactivity level and to easily create or modify a surgical plan. Eachanatomical simulation uses a specific patient anatomical geometry withspecific implantation position and orientation performing a givenrepetitive motion associated with a given activity, such as stairclimbing. Each simulation creates a data point in the database that canbe accessed when attempting to later optimize an implantation plan for asimilar patient. By repeating this simulation for hundreds to thousandsof other geometric combinations and activities, the database can be usedfor a wide combination of native patient geometries, implantation posesand selectable activities.

FIG. 21 is a system diagram for a system 2100 that implements anexemplary embodiment of a surgical planning tool that can be used in astandalone system or as part of a CASS to create or modify a surgicalplan. In some embodiments, a low computational overhead client-serverapproach that uses a pre-populated data store of complex simulationresults allows for on-the-fly optimization and tweaking by a surgeon,thereby allowing selection of various post-operative patient activitiesand changes to implantation factors. In this case of hip arthroplasty,this can include acetabular cup anteversion and abduction angles,bearing type (polyethylene, hard on hard, dual mobility, constrained,resurfacing), femoral head size and offset, femoral stem offset (std.,high, valgus), femoral stem style, femoral stem depth and orientation(version). In the case of knee arthroplasty, the implantation factorsinclude anterior-posterior/lateral-medial placement or cut angles,distal depth, the specific orientation of the distal and posterior cutsfor the condyles to ensure that the compartment gaps are consistentthroughout a range of motion occurring during selected activities andthat ligaments are neither too strained nor too lax, and geometry ofpatella relative to femoral features (PKA/TKA). User device 2102 can bea personal computing device, such as a mobile device, tablet, surgicalworkstation/cart or laptop/desktop computer. For purposes of thisexample, user device 2102 may be described as a tablet device. Ingeneral, user device 2102 is assumed to have low processing abilitiesrelative to a computer that might otherwise be used to run simulationsof patient anatomy. System 2100 is particularly suitable for a system inwhich it would computationally impractical to perform an on-the-flysimulation of a given patient geometry and implant orientation andposition for each selected activity. Within the memory of user device2102 resides application 2103 that guides the surgical planning process.Application 2103 includes a user interface (UI) 2104 and a data store ofuser data and inputs 2106. Application 2103 solicits certain inputsabout a given patient from a user. In some embodiments, application 2103may communicate with the medical records server that includes patientrecords to supply some of this information.

Exemplary information that application 2103 solicits via UI 2104 andstores in database 2106 includes using the camera of user device 2102 tosnap a picture of one or more images of the patient's x-ray/medicalimages (or provides a means by which a user can upload previouslycaptured x-rays/CT/MRI images from medical records or concurrentlycaptured images during a surgery if using the application duringsurgery), entering vital information about the patient, such as height,weight, age, physical build and activity level. In some embodiments,X-rays are the primary medical images, but MRI, CT, or ultrasound imagescan be used in some embodiments. The UI also allows the user to selectinformation about the prosthesis to be implanted and to select variousactivities that the patient would like to participate in postoperatively(e.g., running, golf, stair climbing, etc.). In some embodiments, theability of the user to enter this information using the touchscreen ofthe UI and the camera of the device simplifies the application so thatit does not need to communicate with electronic medical records, whichmay present additional regulatory issues and require additional securityand software modules or the like.

User device 2102 communicates across the Internet 2108 with a server orcloud service 2110. Server 2110 provides backend processing andresources to user device 2102 allowing application 2103 to be alightweight app and user device 2102 to be virtually any available userdevice, such as a tablet or existing surgical workstation. Server 2110maintains a model database 2112 that contains simulation results forvarious implant impatient geometries carrying out predetermined motionprofiles associated with each patient activity. Database 2112 can bepre-populated or continuously updated with additional simulations viasuitable processor, such as a multicore processor of server 2110. Insome embodiments, an additional computer or cluster (not shown)populates this model database, allowing server 2110 to handle incomingrequests from multiple user devices.

In addition to simulation results, model database 2112 can also includeguidelines to assist user in understanding suitable ranges that forselecting appropriate implantation poses. In the case of TKA/PKA, thiscan include selecting appropriate stuffing for the patella, as well asother implant characteristics. For THA, this can include various spinalpelvic motions or sacral tilt angles to assist the user in selectingappropriate anteversion and abduction angles, as well as other implantcharacteristics. Model database 2112 can also include optimalrecommended implantation information, such as optimal implant pose for agiven patient geometry and activity, optimal implant sizes or types oran optimal range that will work with a given patient anatomical geometryperforming a given activity. In some embodiments, simulation resultsalso include simulations with patients having a given geometry andadditional handicaps, such as orthopedic or neurological conditions notfully captured in viewing seated and standing images.

Once a user has uploaded x-ray images (or other medical images) andmanipulated those images to identify certain points and angles withinthose images (or image processing software has automatically identifiedor estimated these angles from the images), patient characteristics,desired patient activities, and optionally starting point implantcharacteristics (e.g., starting poses, implant sizes, bearing type,etc.), server 2110 can consult model database 2112 to find the entrythat best matches the user input and medical imaging geometry. In someembodiments, a large number of independently adjustable variables canmake having a complete database of all possible combinationsimpractical. In these embodiments, the database can include a subset ofpossible combinations of patient characteristics, x-ray/imaginggeometries, and implant characteristics and server 2110 can interpolatea specific result from surrounding entries that are the nearest match toa specific user choice. In some embodiments, the nearest match may beprovided as the result, without interpolation.

In some embodiments, once a variety of simulations have been performedfor various combinations of patient characteristics and geometries, theprocessor can optimize a transfer function to closely match the resultsof the simulation using any conventional means, as described above. Byfitting a transfer function to the results of many simulations, thetransfer function may be provided to server 2110 for quick calculationof results for various activities given user inputs for x-ray andimplant characteristics and patient characteristics, regardless ofwhether that specific combination has been simulated before. This canenable server 2110 to rapidly handle requests for multiple users withouthaving to run potentially tens of thousands of combinations or more insimulation to populate model database 2112. Model database 2112 may beused to store the transfer function, allowing server 2110 to calculatethe result rather than searching model database 2112. In someembodiments, a learning algorithm is used to train a model of patientresponse to each activity for a given geometry to allow quick estimationof response and determination of optimized implant position andorientation at the server, similar to the use of a transfer function.

Exemplary simulations can utilize various simulation tools, includingLIFEMOD™ anatomical modeling software available from LIFEMODELER INC., asubsidiary of SMITH AND NEPHEW, INC., of Memphis, Tenn. Exemplarysimulations are explained in concurrently owned U.S. patent applicationSer. No. 12/234,444 to Otto, et al, which is incorporated herein byreference. Anatomical modeling software can utilize a multi-body physicsmodel of human anatomy that includes bone and soft tissue elements thataccurately model human joints of a given geometry. The specificphysics-based biomechanical model can be customized to the specificinformation from the patient, such as height, weight, age, gender, bonesegment lengths, range of motion and stiffness profile for each joint,balance, posture, prior surgeries, lifestyle expectations, etc. A designof test is created to simulate a variety of anatomical geometries andimplantation angles performing a variety of predetermined motions, eachrelating to a different selectable activity.

FIG. 22 shows the various theoretical angles that can be extracted fromx-ray imaging using a model of hip geometry. Model 2120 is a model ofthe geometry of a hip in standing position, while model 2122 is a modelof a hip in a sitting position. Various angles that can be extractedfrom the geometry of these models include a dynamic sacral tilt or slope(ST, 45° in standing, 20° in sitting), a static pelvic incidence (PI,55° in standing and in sitting), a dynamic pelvic femoral angle (PFA,180° in standing, 125° in sitting), a dynamic ante-inclination angle (AI35° in standing, 60° in sitting). A theoretical angle not shown in FIG.22 that may be extracted and used may include the combined sagittalindex, defined as the sum of ante-inclination and pelvic femoral angle.Not shown in this model is also a static sacral acetabular angle (SAA)at the intersection of the lines that make the ST and AI angles asexplained below.

FIG. 23A is an x-ray of the various points, lines, and angles that canbe extracted from an x-ray image 2130 of the hip of a person standing. Auser can manipulate points 2151 through 2156 using the userinterface/touchscreen, or these points may be automatically generatedthrough an image processing algorithm that can be improved by machinelearning of multiple iterations of user input as the application isdeveloped and used by real world users. Note that in some embodiments,other image types can be used, including MRI, CT or ultrasound imagedata. Once points 2151 through 2156 are placed in the image, lines 2132through 2144 can be automatically placed on the image allowing thevarious angles described with respect to FIG. 22 to be automaticallycalculated by the processor of the user device. Points includesuperior/posterior S1 endplate 2151, inferior/anterior S1 endplate 2152,the center point between hip centers 2153, posterior acetabulum 2154,interior acetabulum 2155, and femoral axis point 2156.

Lines include a horizontal line 2132 originating at point 2151, line2136 (which runs between superior/posterior S1 endplate point 2151 andinferior/anterior S1 endplate point 2152), line 2134 (which isautomatically generated to be perpendicular to line 2136 at thebisection of points 2152 and 2151), line 2138 (defined by the locationof points 2153 and 2155), line 2140 (defined by the intersection pointbetween lines 2134 and 2136 and point 2155), and line 2142 (defined bypoints 2155 and 2156), and horizontal line 2144 (running from point2154). These lines can be automatically generated once points 2151through 2156 are added or extracted from the image. This allows theprocessor to determine the ST (between lines 2132 and 2136), PI (betweenlines 2134 and 2140), SAA (between lines 2136 and 2138), PFA (betweenlines 2140 and 2142), and AI (between lines 2138 and 2144) angles.

As shown in FIG. 23B, the various points 2151 through 2156 (discussed inFIG. 23A) can be manipulated and extracted from an x-ray image 2158 of apatient hip in a sitting position. From these points, lines 2132-2144can be extracted by the processor of the user device/tablet. Once theselines are extracted, the various angles can be calculated. If static PIand SAA angles disagree between sitting and standing, and average can beused. In some environments, a disagreement between these angles canresult in an error or solicitation of additional input from the user. Insome embodiments, a disagreement between these angles can be used toscale or adjust other angles or to refine the placement of points in thesitting and standing x-ray images. These angles can be used by theserver to find appropriate simulation results, as these angles definethe geometry of the anatomy of the patient.

FIG. 24 is a flowchart of an exemplary process 2160 by which a usermanipulates landmarks in an x-ray (or other medical images) to determinethe feature locations, sizes, angles, and spacing that will be used forthe patient geometry to determine joint performance for various patientactivities using a pre-populated statistical model of the behavior ofthe joint for each activity. This can typically be done during apreoperative phase, but can be done in response to images capturedduring a surgery or updated based on new data obtained during surgery,such as be capturing bone features and their relative locations using aprobe that is tracked by a CASS. For example, for a knee arthroplasty,performance can include a measure of the variation of condyle orcompartment gaps, degrees of tension or laxity and ligaments, and thedegree to which the patella tracks in the patellar groove of the femur.At step 2162, the application on the user device loads an image ofpatient anatomy into memory. This can occur by using the camera tocapture an image from a film or screen, by connecting to a stored imagein a local data store or in a medical records system, or by using alocal imaging device to create an image on the fly (such as an x-raytaken during surgery). In some embodiments, the image can be an x-ray,ultrasound, CT, MRI, or other medical images. The file format of thisimage can be any suitable file format, such as PDF, JPEG, BMP, TIF, rawimage, etc. Once the user device loads this image, at step 2164, theimage is displayed and optionally analyzed using image recognitionsoftware. This optional analysis step can utilize any common imageprocessing software that identifies salient features in the image thatcan be useful for identifying landmarks. For example, image processingsoftware may be trained to look for certain geographic features,geometric indicia, or may be trained through any suitable AI process toidentify probable locations for landmarks that are used to determinepatient geometry. At step 2166, the results of this analysis step areoverlaid on the displayed image, placing the landmark points at pixellocations that the analysis software believes are most likely locationsof these landmarks (such as those shown in FIGS. 23A-23B). It should beappreciated that this process described in steps 2162 through 2172 neednot be limited to medical images and can be done automatically, withouthuman interaction with the processor. During the operation, additionalgeometric and location data can be acquired in the CASS through variousmeans, including images or by using a robot arm or surgeon manipulatinga point probe to paint the surfaces of relevant features and registertheir location with the surgical tracking system. This provides anadditional level of improvement of geometric extraction of relevantfeatures that can be used to optimize the surgical plan beyond what maybe possible from preoperative imaging alone.

In some embodiments, image recognition software is helpful, and can be asupplemental/partial or complete substitute for the analysis of anexperienced surgeon or technician. In some embodiments, at step 2168,the user of the software is given the opportunity to manipulate theexact placement of anatomical features, such as femoral axis, sacralgeometry, femoral head and neck characteristics, acetabular cupgeometry, condylar centers and radii, the patellar groove, the patellardome, and tendon and ligament contact points. The exact method by whichthe user manipulates these points will depend on the interface of thecomputing device. For example, on a tablet computing device having atouchscreen, a user can use his or her finger to manipulate and drag theexact placement of these points (which have been placed automatically atstep 2166). In some embodiments, where suitable image processingsoftware has not been trained, the user may create these points fromscratch at step 2168 by tapping the location of these points on theimage after being prompted by the system. In response to moving eachpoint, at step 2170, the display creates and updates the model ofanatomical geometry (such as the relationship of condylar features totibial features and patellar features, as well as information suitablefor determining strain on ligaments and tendons, such as the quadricepangle.) (In some embodiments the step may be held in abeyance until theuser indicates that all points have been moved and the user is satisfiedwith their placement.) This process continues at step 2172 where thesoftware and the user determine whether or not all points and distanceshave been properly manipulated and placed, and the manipulation isrepeated for each point and distance.

Once all points have been moved and the user is satisfied, the user isgiven the option of selecting and loading additional images at step2174. For example, when the surgical procedure being planned is a kneearthroplasty, suitable images can include anterior-posterior andmedial-lateral images of a patient knee in a flexed position and animage in the extended position or any additional poses needed todetermine the geometry of the relevant features. For hip arthroplasty,suitable images can include at least a lateral image of a patient in astanding position and an image in the sitting position. In a kneearthroplasty, suitable images may include a lateral or anterior view ofa knee in both a full extension and bent at some predetermined angle,such as 90°. Additional images may include a patient in the flexedseated position or an extended position while standing. If additionalimages, such as posterior/anterior or medial/lateral views or adifferent position of the joint is available, that image can then beloaded again at step 2162.

Once all images have been loaded, and analyzed, the placement oflandmark points can be manipulated by a user (or software imageprocessing AI). The relevant angles and distances can be calculated fromthe positioning of these points and the method can proceed to step 2176.At step 2176, a user can select the appropriate activities that apatient would like to enjoy postoperatively. These activities can begoverned, for example, by the relative activity level of the patient,age, other infirmities or mobility issues, of the patient. Exemplaryactivities may include standing, walking, climbing or descending stairs,biking, golfing, low-impact yoga, squatting, sitting cross-legged,gardening/kneeling, etc. It should be appreciated that some of theseactivities will likely be relevant to most or all patients, while someactivities may only be relevant to younger or a select subset ofpatients. For example, a running activity may be available that willlikely only be selected for younger or more active patients.

At step 2178, the selection of activities and the patient-specificgeometry calculated from the manipulation of points on patient imagescan be uploaded to a server for (or placed into memory accessible to aprocessor that is suitable for) applying the statistical model createdfrom pre-existing simulation data to calculate suitable results relevantto the surgical procedure. For example, for a hip arthroplasty, theresults may include a map of the centers of pressure within theacetabular cup and range of motion of the joint expressed in a polarplot of the femoral head relative to the acetabular cup. For a kneearthroplasty, the results may include range of motion between the tibialplateau and the femoral condyle's, a contact point map, and stressesexperienced by relevant ligaments, a graph mapping lateral and medialcondylar compartment gaps and ligament tension for a range of flexionangles or for discrete angles, or a graph highlighting the strain onpatellar tendons or ligaments during a range of motion, or how thepatellar dome sits in the patellar groove during that range. For thisfirst pass of step 2178, a default implementation characteristics of theprosthetic implant can be used, (e.g., a 3 degree varus angle and 9-10mm condylar compartment gap). In some embodiments, before step 2178 isperformed, the user also selects the starting implantposition/orientation for a given implant for this initial calculation.

At step 2180, the processor that applies the statistical model to theanatomical information and selection of activities sends this result inthe implantation variables used for the result to the user device fordisplay. In embodiments using a client/server model, the step caninclude sending the results over a network to the user device. In someembodiments, the processor that performs the analysis using thestatistical model can be the same processor as the user device. In theseembodiments, the statistical model is generally refined enough to bestored and analyzed locally. The exact display of these results can bein any suitable graphical manner, such as those interfaces described insucceeding figures.

At step 2182, the user is given the opportunity to choose to manipulatethe implant characteristics (position, orientation, and in someembodiments size and type) manually or to ask for a processor toautomatically optimize the implantation geometry. In some cases, theuser may select an automatic optimization by processor and then refinethe exact resections used for implantation manually to suit thesurgeon's preferences or to place a greater emphasis on certainactivities over others. For example, a surgeon may attempt to optimizean implant so that the patient can return to golfing, but still place anemphasis on the patient's ability to perform everyday activities, suchas climbing stairs and sitting comfortably. In some embodiments, aweighting of activities can be provided in a patient profile that can beused by the optimization algorithm to do this automatically.

At step 2186, if a user has requested processor optimization ofimplantation angles, an optimization algorithm is run by a processor (atthe server or on the user device in non-client/server embodiments) basedon the statistical model. This optimization algorithm can include anysearching algorithm that searches through the statistical database tofind a local or global maximum for the implementation angles thatprovide the best performance or an analysis of the extracted transferfunction to find maximum or minimum results. Criteria used for thissearch can vary depending on the type of implant being implanted. Forexample, in a knee arthroplasty, the algorithm can target a reasonablerange of condylar compartment gaps for medial and lateral compartments,identifying the most consistent gap through the flexion range, whilemaintaining ligament tension within a suitable range, in accordance withguidelines and identify the femoral implant pose and patella packing andattachment constraints that best allow the patella dome to track in thepatellar groove with minimal soft tissue strain. For hip arthroplasty,anteversion and abduction angles that provide the center-of-pressureprofiles and range-of-motion profiles that run the lowest risk of edgeloading or dislocation can be identified by minimizing the amount ofpressure near edges of the acetabular cup and minimizing the amount ofrange of motion that risks impinging on the edges of the cup for each ofthe selected activities.

In some embodiments, this optimization procedure can include iterativelychanging resection placement and orientation until a suitable or bestresult is achieved. Once this optimization has been completed by theprocessor that handles the statistical model, the results can bereceived and displayed by the user device at step 2180. If the userwishes to manually alter the angles of implantation, the user will canupdate the selected angles of implantation through the user interface atstep 2184. The selections can then be uploaded to the processor handlingstatistical model at step 2178. In some embodiments, the user can alsochange implant bearing type or liner options. For example, a user canswitch from a 0 degree neutral XLPE liner to a 20 degree anteverted XLPEliner, allowing the user to experiment with range of motion and centerof pressure. Similarly, a user can switch from a conventional bearingsurface (XLPE) to a dual mobility system that offers increased head sizeand jump distance that allows for greater stability in the joint.

Once a user is satisfied with the results of manipulation or automaticoptimization, at step 2188, the user device updates the surgical plandisplayed to a user or within a CASS, allowing a robotic surgical systemto prepare to implement the chosen implant locations and orientationsbased on method 2160. In embodiments where a cutting guide is used, step2188 can include sending the surgical plan (that includes the specificmap of relevant patient bone surfaces and the specific location ofresection cuts relative to these surface) to a manufacturing system thatmanufactures patient-specific cutting guides and requesting that theseare 3-D printed prior to the operation. In some embodiments, where arobotic arm will position or hold a non-patient specific cutting guide,the surgical plan can include requesting the appropriate cutting guideto be provided to the surgeon for the operation and programming therobotic arm to place the cutting guide at the specific predeterminedlocation and orientation.

FIG. 25 is an exemplary table 2200 that can be displayed to a user toassist in understanding how pre-operative anatomy compares to expectedranges for a healthy patient. This can help guide a surgeon inunderstanding the appropriate surgical plan for use with a CAS system.Table 2200 shows the results of the calculation of various angles insitting and standing images using image analysis or manipulation ofpoints as described above for planning a hip arthroplasty. Column 2202lists the various angles that are calculated for each image includingST, PI, PFA, and AI. Column 2204 shows the results of the analysis ofthe angles based on the points identified in a standing image. Column2206 is an analysis to be performed by the server or the user devicebased on a list of acceptable ranges for these angles, such asguidelines shown in table 2114 in FIG. 21. In this example, the sacraltilt has been identified as normal, while pelvic incidence, the pelvicfemoral angle, and anti-inclination angle have been identified asabnormal, being outside the normal range expected for a healthy patient.Column 2208 shows the angles that have been calculated based on thepoints identified in a seated x-ray image. Column 2210, like column2206, is an identification of whether or not each angle is within anacceptable range for a healthy patient. Section 2212 is a comparison ofvarious angles between standing and seated positions (2204 and 2208),including a difference in sacral tilt and anti-inclination. These deltasare then used for determining whether or not spinal-pelvic mobilityfalls within a normal range and where the spinal-pelvic balance lies.This information can be useful in guiding the selection of implantationorientation of a prosthesis to improve mobility and balance patient hipgeometry. This information can be considered by the surgical user or canbe available on demand as a teaching tool. As discussed, the angles forstanding and sitting are used by the patient model database inconjunction with anteversion and abduction angles for the acetabular cup(or other implant characteristics) to determine range of motion andcenter of pressure results from the statistical database of simulatedresults.

FIG. 26A is an exemplary user interface 2220 for selecting individualactivities that are relevant to a given patient and displaying theresults from the statistical database based on previous user input abouta patient, including x-ray landmark mapping for that patient. A user mayselect from various individual activities 2222 and can manipulate theabduction and anteversion angles 2224 and 2226 for the implant (notethat these exemplary angles may vary from real-world values for givenimplants). A center-of-pressure heat map 2228 is an aggregate of all ofthe selected individual activities 2222 based on the patient geometryfrom x-ray imaging and the manipulation of abduction and anteversionangles 2224 and 2226. (Not shown are options to change liner and bearingcharacteristics, which may be available in some embodiments.)Manipulating abduction angle 2224 or anteversion angle 2226 will movethe heat map relative to the circle, which represents the extent of theacetabular cup, based on the statistical model. Placing pressure toonear any of the edges of the acetabular cup can result in edge loadingthat can lead to premature wear or failure of a hip implant or can leadto dislocation in some cases. Similarly, range of motion map 2230 is anaggregate of a patient's range of motion that is used in each individualactivity and how that motion translates to the interaction betweenfemoral head and acetabular cup. The extents of this range of motionshould be confined to the circle that represents the extents of theacetabular cup. Any range of motion that falls outside of these extentswill result in a high risk of hip dislocation or impingement thatimpedes the motion needed for that activity.

In some embodiments, an optimized button 2232 is presented to a userthat allows the user device or server to automatically change abductionand anteversion angles such that it optimizes the placement of thecenter of pressure and range of motion within the circles. (In someembodiments, this automatic recommendation can include implantselection, such as implant size, bearing type, liner type, femoral headcharacteristics, etc.) This can be done iteratively using any suitablealgorithm to adjust these angles to improve the area of the centerpressure and range of motion that falls within the extents of theacetabular cup circle. Heuristics that are used can include maximizingthe distance between pressure points and the edges of the acetabular cupfor the center pressure and maximizing the distance between the extentsof the range of motion in the edges of the acetabular cup circle,maximizing the mean distance, maximizing the minimum distance, etc.

In some environments, a diagram 2234 is presented to the user thatchanges as abduction and anteversion angles are manipulated, to providevisual feedback to a user on how these angles affect acetabular cupplacement. Not shown in interface 2220 is a button that exists in someembodiments to finalize and save the abduction and anteversion anglesand load these into the surgical plan of the CAS system.

FIG. 26B shows how the user interface changes when only a subgroup ofindividual activities 2236 is selected. Rather than a large heat map orlarge variety of ranges of motion, the center-of-pressure map 2238 showsa more limited center of pressure that is attributed to just theseactivities. Similarly, range-of-motion map 2240 shows the range ofmotion used by just these activities. Contrast these maps to maps 2228and 2230 (FIG. 26A), which illustrates the fact that a broader range ofactivities results in a broader heat map of the center of pressure and abushier range of motion. For some patients, where mobility is limited bylifestyle or other factors, only certain activities may be important,allowing a user to more easily optimize implant selection and acetabularplacement (in hip arthroplasty applications) to ensure that the user cansuccessfully do these activities. With some patients, it may not bepossible given other constraints to place an acetabular cup in such away that all activities are possible. This can be due to abnormal hipgeometry determined from the (x-ray) images or due to external mobilityissues, such as spinal immobilization. In some embodiments, the surgeonor user is able to make recommendations to their patients on whichactivities or positions may pose a risk for impingement, dislocation, orexcessive wear to their artificial hip.

FIG. 26C is a user interface showing an embodiment whereby hovering overor otherwise temporarily selecting an individual activity 2242 amongst agroup of activities can be used to highlight the contributions of thatactivity to the center-of-pressure map 2228 and range-of-motion map2230. By selecting stair descent, the center of pressure map associatewith that single activity and the range of motion map associated withthat single activity can be highlighted within the respective maps. Inthis example, heat map 2244 is temporarily highlighted oncenter-of-pressure heat map 2228, showing the concentrated portion ofthe map that is attributable to stairclimbing descent. Similarly, arange of motion profile 2246 can be temporarily highlighted withinrange-of-motion map 2230 to show the contributions to therange-of-motion map that are attributable to this activity.

FIG. 26D is an illustration of how the user interface changes asabduction and anteversion angles 2224 and 2226 are manipulated. In thisexample, all activities are selected and the abduction angle is reducedby 12°, while the anteversion angle is reduced by 2°. This results in abroadened heat map 2248 of the center pressure, which creates anincreased risk of edge loading of the acetabular cup, while at the sametime shifting of the range of motion map 2249 of these activities awayfrom the center of the acetabular cup, which creates a greater risk ofdislocation or injury. The result in FIG. 26D is less desirable than theresultant FIG. 26A.

FIG. 27 shows the impact of adding multiple activities together tocreate an aggregate center of pressure heat map and range of motion map.In this example, stair climbing and stair decent result in a broadercenter of pressure and fuller range of motion than individual activitymaps.

FIG. 28 is a flowchart illustrating an exemplary creation of astatistical model database that can be queried using patient anatomy andimplant characteristics and a selection of patient activities toestimate performance of the implant. This statistical model database iscreated by performing (typically) hundreds to tens of thousands ofsimulations using a model of joint anatomy (such as a multi-bodysimulation) that models the behavior of each component of the joint asthat joint is moved through a motion profile that is associated with atleast one common motion that will occur in a patient joint when thepatient participates in a given activity. In some embodiments, thismotion profile may be modeled by using motion capture technology on asample subject performing a given activity. By capturing the motion ofthat individual (or individuals) performing the activity, an accuratemodel of the motion that each joint will undergo during that activitycan be created. The model of individual joint motion can then bereviewed to identify exemplary repetitive motions that a person willlikely experience when performing an activity. This motion profile modelcan then be used to guide each individual simulation for that activity,where anatomical geometry and implant characteristics are varied tocreate a design of experiment that includes a range of anatomicalgeometry and implant characteristics.

In some embodiments, method 2260 begins at step 2262, where motioncapture is used on an individual actor while that individual performsexemplary tasks associated with each activity to be modeled. Forexample, reflective marks can be placed on a model's body as he/sheclimbs and descends stairs in front of one or more cameras. At step2264, a processor can then extract the motion that these marks undergoduring the activity. By using a model of anatomy and where these marksrelate to the individual's joints, the motion profile that each jointundergoes during this activity can be extracted. Any suitableconventional motion capture technology can be used for this step. Forexample, where a hip or knee motion profile is being created, at leasttwo cameras can capture reflective marks on a model's leg, iliac crest,torso, femur, tibia, patella, malleolus, medial and lateral condyles,etc., as the individual moves. Hip and knee profiles can be createdsimultaneously with sufficient marks. As the individual moves her leg,lifting and placing her foot during the activity, the degree of motionand rotation within each degree of freedom can be calculated as the hipand knee move. This can then be used by the processor to estimate thedegree of relative motion of the individual components of the joint.

At step 2266, a processor performing the simulation of anatomical modelswill load an anatomical model. An exemplary anatomical model can be amultibody model of bone and soft tissue components that accuratelymodels the behavior of each of these components of an anatomical joint.By using a multibody model, simulation times can be expedited overfinite element analysis models. In some embodiments, finite elementanalysis models can be used, as well. An exemplary multibody simulationtool for use with modeling joints includes the LIFEMOD™ softwareavailable from LIFEMODELER INC. This anatomical model, once loaded, canbe customized to a given geometry. For example, component sizes can beadjusted to achieve any joint geometry to be simulated.

At step 2268, sample joint parameters (such as anatomical geometry andimplant size, type, position, and orientation) are selected to be usedfor the next simulation. The selection of this joint geometry can bebased on a preplanned design of experiment or randomly assigned in aMonte Carlo style simulation. Components in the model can then be sizedappropriately to match the geometry for this given experimentalsimulation. For example, in the TKA context, the selected geometry caninclude any reasonable combination of distal and posterior cuts, as wellas other implant features for the implantation of a sample prosthesis.In some embodiments, the geometry selected can include additionalpatient information, such as abnormal motion constraints due to otherinfirmities or deformities, or other common medical comorbidities. Insome embodiments, age and weight can be added to the model to addresschanges in response of various components that may vary with the age orweight of the patient (e.g., less compliant or thinner soft tissue).

At step 2270, a simulation of the model that has been sized according tothe experiment parameters is performed using the joint motion profilefor a given activity. This results in several quantifiable results, suchas soft tissue impressions and tensions, pressures between componentssuch as femoral heads and acetabular cups (for hip arthroplasty) andcondylar compartment gaps and patella tracking over a range of motionbetween components (in knee arthroplasty). The simulation profile usedto run the simulation in step 2270 can define which of these resultsshould be generated and recorded. At step 2272, once a simulation hascompleted, the results for that given combination of anatomical andimplant characteristics and motion profile can be stored.

At step 2274, the processor determines whether additional parameters,such as anatomical geometry, implant design, orientation, and position,should be simulated. In many embodiments, a simulation profile thatdefines the design of experiment or extensive Monte Carlo simulationwill define hundreds to tens of thousands of different simulations thatshould be carried out. Accordingly, the cycle of the steps 2268 through2274 should be repeated many times. Once a sufficient number ofsimulations have been completed, method 2260 can proceed to step 2276,where the processor mines the results of these multiple simulations tocreate a transfer function or model of the behavior of the joint basedon various geometries. This model or transfer function creates astatistical model for that activity and that joint that accuratelyestimates the performance as a function of geometries for the patientanatomy and implantation of a prostheses. This can be accomplished byperforming a statistical fit of a polynomial function that maps inputimplant values to output values, in some embodiments. In someembodiments, a neural network or other AI strategy can be used to createa heuristic model of implant characteristics to performance output. Step2276 can be repeated as additional simulation results are added to thedatabase. In some embodiments, measured real-world performance valuescan be added to the database and mined similarly to simulated results,as additional implantation surgeries are performed and monitoredclinically.

At step 2278, the processor stores and maintains the statistical modelof joint performance as a function of anatomical geometry andimplantation features (position, orientation, and types of implantsselected). This stored model can be updated as additional simulationdata becomes available. In some embodiments, the resulting model can becomputationally lightweight (e.g., a transfer function), allowing miningand manipulation of this model to be performed by any processor in thesystem, at the server or on a user device. At step 2280, thisstatistical model is mined by the processor to identify optimal implantvalues that optimize performance based on predetermined heuristics. Forexample, in a model of hip arthroplasty, the maps of center of pressureand range of motion can be optimized to minimize the risk of edgeloading or dislocation, minimizing the range of motion and degree ofpressure that falls near the edges of the acetabular cup for a givenactivity. For example, in a model of knee arthroplasty, the heuristicscan include gaps, patellar tracking, and ligament tensions being nearnormal anatomical values during a given simulated activity. Thisoptimization can be through any statistical or AI approach, includingfinding a best fit that that best approximates an ideal anatomicalmodel. These optimization values can then be added to the maintainedmodel at step 2278.

Once the model has been stored and an optimization has been performed toassist in identifying optimal implantation features (position,orientation, implant type, implant size) for a given patient anatomy andactivity, at step 2282, the model can be queried by a processor inresponse to user interaction, such as during a preoperative orintraoperative planning stage. This allows a surgical user to access thestatistical database model to develop a surgical plan that can then beused by a CASS or displayed to the user.

FIG. 29 is a flow chart of an exemplary method 2400 for creating apreoperative plan using a simulation database and patient specificgeometry extracted from images and (optionally) motion capture. The samemethod can be used to optimize a preoperative plan for hip (or other)arthroplasty, but will be discussed in the context of a kneearthroplasty. At step 2402, the surgical planning system collectspreoperative imaging, such as MRIs, CT scans, x-rays, and ultrasoundimages. These can be done at any time in advance of the surgery. Imagefiles can be loaded into a database specific to that patient for featureextraction. In some embodiments, at step 2404, motion capture techniquescan be used in a preoperative visit to capture a patient's gait byaffixing markers to various points on their leg and observing therelative motion of markers as the patient performs various motions. Thiscan be used to provide supplemental detail about the geometry of thepreoperative state of the patient's knee. At step 2406, imaging isanalyzed using geometric techniques for image analysis (and any motioncapture data is analyzed based on a motion model of human anatomy).Software performing the image analysis can use any suitable featureextraction technique to identify predetermined features in the patientimages and create a three-dimensional model of the patient's knee basedon a plurality of images that includes information such as sacral/pelvicgeometry, femoral head/neck and acetabular cup geometry, femoral andtibial axes, condylar centers and sizes, existing condylar gaps, patellasize, and its existing relationship with preoperative soft-tissuetensions.

In parallel, and typically prior to these steps, a statistical model iscreated at step 2408 that accounts for various possible patientgeometries for a wide range of patients. Hundreds to hundreds ofthousands of simulations for various possible patient geometries can beperformed off-line to create the statistical model. The simulation datacan then be mined to create a transfer function or simplified model fora given geometry, allowing performance to be determined or estimated forany given patient geometry. At step 2410, when an individual patient'sanatomical geometry has been determined (step 2406), the statisticalmodel is loaded.

At step 2412, the statistical model is used to explore possiblecorrections to a patient's given anatomical geometry. This can take onvarious suitable approaches as known in the art, such as applyingartificial intelligence searching and matching algorithms to identifyincremental improvements to implantation geometry to correct patientinfirmities. For example, AI can be used to identify a design-of-test toidentify likely candidate changes to improve the mechanics of thepatient's joint. Similarly, a Monte Carlo simulation may be used toinvestigate random changes to implantation geometry using thestatistical model to identify the best performing options for implantinga TKA prosthetic. In some embodiments, at step 2412, many (e.g., dozensor thousands) variations are used to identify the optimal solution toimplantation. Because this step is preoperative, the processing time oroverhead can be quite substantial, if needed. Various attributes can bechanged, including the pose of tibial and femoral components, patellarpacking and dome geometry, etc., to find a solution that optimizesperformance of the patient knee that takes into account both thetibiofemoral and patellofemoral joints, rather than viewing of thepatella as an afterthought, as is often the case with existing surgicalplans. In some embodiments, the statistical model created at step 2408can simply be queried for an optimal implantation pose based on thestarting patient geometry, such as by using a transfer function with anAI model. In some embodiments, the simulation variations undertaken atstep 2412 can be specific to different patient activities, allowingactivity-specific optimization for an individual patient.

In some embodiments, the purpose of step 2412 is also to create apatient-specific model that takes into account the imprecise nature ofpreoperative data. For example, preoperative images and motion capturemay create an estimate of patient geometry that is inexact. Datacollected during a surgery can later be used to improve the model ofpatient anatomical geometry. By considering the plurality of variationson not only the implantation poses, but also the patient anatomicalgeometry (within a range), a patient-specific model can be created sothat changes to the surgical plan can be made on-the-fly during surgerybased on additional data or based on a request by a surgeon to alter theimplantation plan,

Once a plurality of variations on the patient geometry and implantationpose are considered, a patient specific model can be stored at step2414. This model can be stored in non-volatile memory, allowing it to beaccessed by the CASS during an operation. This will be discussed withrespect to FIG. 30. Any suitable amount of information or format may beused, with the goal of simplifying any processing or simulation that isdone during the patient operation, such that changes can be handledon-the-fly without slowing down the surgery.

At step 2416, the processor can create an optimized surgical plan basedon an optimization of the implantation poses created at step 2412 and apatient profile. For PKA/TKA, this plan can include implantation posesfor femoral and tibial components (including the resections needed toaccomplish these poses), as well as a plan for packing the patella andany changes to the dome or patellar buttons that may be needed toachieve the relationship between patella and femoral components as partof the arthroplasty. At step 2420, this optimized preoperative plan isprovided to the surgeon and to the CASS to prepare for surgery.

In some embodiments, an additional step, step 2418, can be undertaken,whereby variations of the preoperative plan are created to take intoaccount possible variations in patient anatomical geometry that may bediscovered during surgery, as well as any reasonable deviations from thesurgical plan that may be undertaken by a surgeon during the operation.These variations can be associated with expected performance outcomesfor the surgically modified joint. This can make it easier for the CASSto provide recommendations during surgery based on additional patientdata observed in the surgical theater or based on requests by thesurgeon. Effectively, this can create a very low computational load forproviding recommendations or calculating expected performance impacts ofadditional data or decisions during surgery.

FIG. 30 shows an exemplary method 2430 for updating the surgical plan orfor providing recommendations to a surgeon during a surgery. Once apreoperative plan is created, additional information may be gathered inthe surgical theater that may be useful for updating this plan.Similarly, surgeons often rely on their own experience and expertise andmay adapt the surgical plan based on what they find during the surgeryor a disagreement with the AI-generated recommendation. At step 2432,the CASS collects intraoperative imaging and probe data. Intraoperativeimaging may include any conventional medical imaging, such as ultrasounddata. This intraoperative imaging can be in addition to preoperativeimaging. This may provide additional detail or updates to a model ofpatient geometry. Similarly, once patient tissue is opened, a probe maybe used to “paint” various surfaces of patient bone, as explainedthroughout. This can provide additional detail about the exact 3-Dnature of patient tissue surfaces that may be more accurate than themodel created from a two-dimensional or three-dimensional imaging. Atstep 2434, the processor analyzes intraoperative imaging and probe datato supplement the model of patient geometry by identifying specificfeatures being observed in the surgical theater and comparing to theexisting model. Once the geometric model of patient anatomy is updated,a surgical user can request an update to the plan at step 2436 (or forgorequesting an update, skipping to step 2444).

In response to this new data and user request, at step 2440, theprocessor selects the optimal plan based on the new anatomical geometrymodel. This is accomplished by first loading patient specific models orplans from memory at step 2438. Because of the time critical nature ofintraoperative recommendations, the models and plans loaded at step2438, in some embodiments, are patient specific, such as those createdin FIG. 27. This limits the universe of possible geometric changes tothe most relevant, based on a model of patient anatomy prior to surgeryto expedite processing recommendations for changes to the plan. At step2440, an optimal plan can be chosen via any conventional computationalapproach from the available plans and models, based on the observedpatient anatomy. At step 2442, this updated recommendation to the plancan be presented to the user via a user interface of the CASS and to theCASS to update the plan it will assist in implementing.

At step 2444, the processor can begin monitoring user actions orrequests by the user for plan updates. Monitoring of user actions cancome via the CASS, such as by monitoring the actual resections that areundertaken by the surgeon. For example, a deviation from the surgicalplan in the resection may necessitate a recommended change to otherresections to limit the impact of the deviation. A user may alsomanually change the surgical plan, such as a surgeon intentionallydeviating from a recommended plan, based on expertise and experience.These deviations to the plan will be noted by the processor, which willprovide feedback to the user. If at any time, the user would like arecommendation for a change of plan, the user interface can be used torequest a recommendation, which repeats step 2436.

At step 2446, the processor estimates the performance impacts of thedeviation from the optimal plan provided at step 2440 and providesfeedback to the user based on this estimate, at step 2448. For example,for a PKA/TKA a change to a patellar/implant pose that occurs during thesurgery can necessitate changes to the patella. At step 2448, a GUI maycause an indicator of the patella to flash or change color to indicate apotential problem. For a hip revision/THA, hip components can flash orchange color.

AI Enhanced Cutting Guides

Some embodiments utilize the simulation database and AI guided planningprocesses described herein to improve robotic or computer-enhancedsurgical planning. However, such surgical systems may be limited orunavailable. Accordingly, in some embodiments custom, patient-specificcutting guides may be manufactured in accordance with the sameconcepts—their creation guided by applying a simulationdatabase/statistical model to preoperative imaging,

Some embodiments use these concepts to create a pre-surgical plan oradjust it on the fly intraoperatively, as more data is acquired in thesurgical theater. In some embodiments this same process can be used withpre-operatively manufactured cutting guides. For TKA/PKA, once the idealposition and orientation of the distal and posterior cuts for a kneeimplant are identified, the surgical plan can then be sent to a computerassisted surgical system or other suitable system for implementation ofthe surgical plan. For example, in some embodiments, the preoperativeplan can be used with a custom cutting guide system whereby images ofthe surfaces of patient femur and tibia are used to identify a patientmatched surface to form the base of a 3-D printed cutting guide. Thedistal and posterior cuts, as well as any secondary cuts needed forfitment, can be added to this patient matched cutting guide. Once thecutting guide is printed (or otherwise manufactured), the cutting guidescan be sent to the surgeon for use during implantation. Any suitableconstruction for a customized cutting guide can be used. Exemplaryconstruction of a customized patient matched cutting guide can be seenin co-owned US Patent application publications US 2012/0116562 and US2014/0018813, which are hereby incorporated by reference in theirentirety. This can cut down on the risk of a surgeon impreciselyaligning the cuts in accordance with the preoperative surgical plan. Insome embodiments, a cutting guide is less customized to patient surfacesand can be used with a computer-assisted surgical system. For example, arobot arm may precisely place and hold a non-patient matched cuttingguide that is selected in accordance with the preoperative surgicalplan. Once a robot arm places and holds the cutting guide in place, oronce a patient-matched cutting guide is affixed to a patient bone, asurgeon can use an oscillating or rotary tool to resect patient bone inaccordance with the cuts of the surgical plan to ensure precisealignment of the prosthetic implant.

TKA/PKA are especially well suited for custom cutting guides (orselection from a plurality of available cutting guides to find one thatis best suited for a patient). FIG. 31 is an illustration of howrelevant anatomical geometry can be extracted from images, such asx-rays, for a knee joint for creation or selection of the appropriatecutting guide. Medial-lateral image 2520 and posterior-anterior image2522 show a patient knee in partial flexion. This process should berepeated for at least two or more knee poses (e.g., extension, 30-60-90degrees flexion). From the lateral view, distal and posterior condyleradii can be determined from the x-ray image (2524 and 2526,respectively). These radii can be determined graphically, eitherautomatically through image analysis and geometric fitting to the imagevia a basic searching algorithm to provide a best fit of radii, ormanually by a surgeon using a graphical user interface, such as atouchscreen. This can be done manually or via any conventional imageprocessing algorithm that fits circles to images. Here, circle 2524 isfit to the distal condyle radius and circle 2526 is fit to the posteriorcondyle radius. Once the circles are fit, pixel locations identify thecenter point and radius of each circle relative to an anatomical axis2528 of the femur, which can be determined by bisecting the boundariesthat define the anterior and posterior surfaces of the femur. The axisof the femur 2528 can be automatically added through similar imageanalysis or manually by a surgeon.

From the anterior-posterior image, medial and lateral condylarcompartment gaps 2532 and 2534 can be determined by fitting rectangles(or parallel lines) to the boundaries of the femur and the tibia. Thesegaps are the space between the condyles and the bed of the tibia. For abalanced knee, these gaps should be matched and consistent through arange of motion. The distances of gaps 2532 and 2534 can be determinedthrough any graphic processing algorithm that is suitable for measuringdistances between features, or may be measured by manipulating pointsthrough user interface for a surgeon to identify the edges of thecondyles and tibia for a distance measurements. An anatomical axis 2530of the tibia can be determined by bisecting the medial lateral edges ofthe tibia (while anatomical axis 2528 can be similarly created).Comparing these axes reveals the degree of varus/valgus in the joint.

The process shown in FIG. 31 can be repeated for different angles offlexion and compared. Condylar center points and radii can be averagedfrom the different images, while the changes in the gaps can be plottedto better understand the degree of gap change and asymmetry in thejoint. Medial-lateral force can also be applied to either determine anestimate of instability or to capture images showing how gaps areaffected by the force to better understand the degree of ligamentlaxity.

Using Simulation to Improve Patella Performance

Traditionally, surgeons focus primary attention during a knee procedureon the tibiofemoral joint of the knee, ensuring that the condylar gapsare consistent throughout the range of motion and that ligament strainis below a threshold. The patellofemoral joint can then be adjusted onceof the tibiofemoral joint has been corrected. While this approach cancreate successful outcomes, the outcome is not necessarily optimized fora given patient. In some embodiments, simulation is used tosimultaneously plan the tibiofemoral joint and the patellofemoral jointsimultaneously to create a surgical plan that optimizes both joints inthe knee.

The patellofemoral joint consists of a patella having a dome that ridesin the patellar groove between the condyles of the femur. The patella isconstrained by a patellar ligament, quadricep tendon, and laterally bythe retinaculum. The angle at which the quadricep tendon sits relativeto an axis of the knee is described as a quadricep angle or q-angle.When riding in the patellar groove, the dome of the patella experiencessheer stress, as it is being pulled relative to the groove(medially/laterally), creating shear.

During the arthroplasty, once the femoral and tibial prosthetic pieceshave been implanted and secured to patient bone, the surgeon can adjustmanually how the patella dome rides in the patellar groove between ofthe femoral condyles. To do this, the surgeon will observe the laxity ofthe quadricep in the patient's passive knee and move/rotate the patelladome to add a desired amount of tension to the quadriceps while keepingthe q-angle within a desired range. This process is typically referredto as packing the patella, and can be achieved by adjusting the tensionof patellar ligaments and tendons to adjust how the dome of the patellatravels in the groove, pushing the patella outward and increasingtension on the quadricep tendon. To move the patella into the desiredgeometry, a surgeon can perform tissue releases of any ligaments ortendons that are constraining the patella. Packing, by adjustingtissue/inserts between the patella and the condyles on either side ofthe trochlear groove, can reduce the amount of available strength oroverly strain of the retinaculum if too much packing is done.

Because the amount of packing can reduce performance of the repairedknee, manual adjustment while all muscles are relaxed can arrive at asuitable geometry that may not have optimal performance for a repairedknee. Laxity is a very qualitative/subjective measure, especially in apassive muscle in surgery. Simulation can improve this. In comparison toa flexed knee in a passive state on the surgical table, simulation canadd additional data to better understand the performance of the repairedknee. Simulation can add loads, such as body weight and quad strain, anda motion profile, such as gait where multiple muscle groups fire.Simulation can also consider deep knee bends, where the primary force isfrom extensor muscles.

Embodiments use simulation data to assist in the patellar packing stepof surgery and in considering patellar performance to determine thetarget implantation pose of the tibial and femoral components. Inexisting surgical planning processes, it is common to optimize thefemoral and tibial implantation poses without detailed consideration ofthe patella, relying on subsequent packing of the patella once thefemoral and tibial poses are constrained. By utilizing simulation data,the preoperative plan and adjustments to the plan during surgery canconsider simulated motions with real-world loads and muscle tensions andthe expected patellar performance (and packing plan) before makingrecommendations for the implantation poses of tibial and femoralcomponents (and the resections needed to achieve the target implantationposes).

FIGS. 32-33 illustrate an exemplary surgical system CASS 2600 that caninclude a supplemental system to aid in gathering additional informationabout patella tracking in a patient knee during a surgery. Thisadditional information about the patella tracking in the pre-modifiedknee can be used to refine the anatomical model to provide a refinedrecommendation of implantation pose and patella packing strategy usingthe statistical model or transfer function on the fly during surgery.The exemplary surgical system 2600 can be configured to intraoperativelyobtain positional data relating to a range of motion of a knee that willbe subject to a surgical procedure. This positional data can correspondto discrete angles of flexion or extension of the operative knee. Incertain embodiments, the system 2600 can include a surgical navigationsystem/CASS 2610 and a probe 2620, as described with respect to the CASSthroughout. In operation, the knee of a patient is moved through a rangeof motion, so that the position of various anatomical components such asthe femur, the tibia, and the patella can be tracked.

In some embodiments, the surgical navigation system 2610 can beconfigured to employ a patella tracking component 2630. The patellatracking component 2630 can be configured and implemented as an integralsystem or component within the surgical navigation system 2610 and mayshare hardware/software or implemented as a standalone component thatconnects to the surgical navigation system 2610. It should beappreciated that embodiments of the described subject matter can beimplemented by various types of operating environments, computernetworks, platforms, frameworks, computer architectures, and/orcomputing devices.

The surgical navigation system 2610 and/or the patella trackingcomponent 2630 can include one or more processors and memory devices, aswell as various input devices, output devices, communication interfaces,and/or other types of devices. The surgical navigation system 2610and/or the patella tracking component 2630 can include a combination ofhardware and software.

The surgical navigation system 2610 and/or the patella trackingcomponent 2630 can implement and utilize one or more program modules.Generally, program modules include routines, programs, objects,components, data structures, and/or the like that perform particulartasks or implement particular abstract data types.

The surgical navigation system 2610 and/or the patella trackingcomponent 2630 can be implemented by one or more computing devicesconfigured to provide various types of services and/or data stores inaccordance with aspects of the described subject matter. Exemplarycomputing devices can include, without limitation: personal computingdevices, web servers, front end servers, application servers, databaseservers, domain controllers, domain name servers, directory servers,and/or other suitable computers. Components of the surgical navigationsystem 2610 and/or the patella tracking component 2630 can beimplemented by software, hardware, firmware or a combination thereof.

The patella tracking component 2630 can include a processor 2632, memory2634, input devices 2636, probe interface 2638, measurement database2640, and output device 2642. The input devices 2636 can be configuredand implemented to receive instructions from a surgeon beforeimplementing a surgical plan.

The patella tracking component 2630 can be configured to characterizethe anterior surface of the patella with the probe 2620 to relate theanterior geometry to the position of the posterior apex. The patellatracking component 2630 can implement the processor 2632 to communicatewith the probe 2620 through the probe interface 2638 to obtainmeasurements of the position of the patella at discrete increments. Theincrements can represent discrete amounts of flexion and extensionmeasured in degrees or other suitable units.

The processor 2632 can be configured to execute software instructionsstored in memory 2634 to determine, for example, the posterior apex ofthe patella by using measurements from the probe 2620 before the patellatracking component 2630 evaluates patella movement. In some embodiments,the posterior apex of the patella can be determined with the probe 2620,so the relative position of the posterior apex to the anterior patellageometry can be recorded in the measurement database 2640.

The probe 2620 can be a handheld point probe, such as is shown in FIG.25B. The probe 2620 is used to identify certain landmarks and tocharacterize surfaces. Alternatively, the probe 2620 can be a pointprobe that terminates in a curved tip to identify hard-to-reachlandmarks that would otherwise be covered or blocked by, for example,soft tissue. The probe 2620 can be used to relate the position of theposterior apex to the anterior geometry.

In certain embodiments, the processor 2632 can approximate movement ofthe posterior apex during a full flexion and extension by locatingspecific anatomic features on the anterior patella or by probing theanterior patella surface at different increments. For example, theincrements can include 30°, 60°, and 90° of flexion.

While some embodiments use a handheld probe to locate landmarks duringthe range of motion, some embodiments utilize fiducial marks that aretemporarily affixed to the patella to automatically track the motion andpose of the patella relative to tibia and femur or place temporaryvisual marks on one or more location of the patella to index thelocation that should be contacted by the probe to expedite the patellatracking process.

The output device 2642 can generate position measurements of the patellain various stages of extension and flexion. In certain embodiments,FIGS. 34A and 34B depict a display illustrating the collection ofposition data relating to the location and orientation of the patellathrough a range of motion from nearly full extension 2650 in FIG. 34A tonearly full flexion 2652 in FIG. 34B.

Co-owned Patent Application No. PCT/US2019/045536 (“Patella TrackingMethod and System”) describes various additional ways that a patella'sphysical characteristics and can be identified and itsposition/orientation determined during a surgery using a CASS, and ishereby incorporated by reference in its entirety.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

An executable application, as used herein, comprises code ormachine-readable instructions for conditioning the processor toimplement predetermined functions, such as those of an operating system,a context data acquisition system or other information processingsystem, for example, in response to user command or input. An executableprocedure is a segment of code or machine readable instruction,sub-routine, or other distinct section of code or portion of anexecutable application for performing one or more particular processes.These processes may include receiving input data and/or parameters,performing operations on received input data and/or performing functionsin response to received input parameters, and providing resulting outputdata and/or parameters.

A graphical user interface (GUI), as used herein, comprises one or moredisplay images, generated by a display processor and enabling userinteraction with a processor or other device and associated dataacquisition and processing functions. The GUI also includes anexecutable procedure or executable application. The executable procedureor executable application conditions the display processor to generatesignals representing the GUI display images. These signals are suppliedto a display device which displays the image for viewing by the user.The processor, under control of an executable procedure or executableapplication, manipulates the GUI display images in response to signalsreceived from the input devices. In this way, the user may interact withthe display image using the input devices, enabling user interactionwith the processor or other device.

The functions and process steps herein may be performed automatically orwholly or partially in response to user command. An activity (includinga step) performed automatically is performed in response to one or moreexecutable instructions or device operation without user directinitiation of the activity.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of the invention to accomplish the same objectives. Althoughthis invention has been described with reference to particularembodiments, it is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the invention. No claimelement herein is to be construed under the provisions of 35 U.S.C.112(f) unless the element is expressly recited using the phrase “meansfor.”

1.-15. (canceled)
 16. A system for creating an activity-optimizedcutting guide for a surgical procedure, the system comprising: one ormore processors; and a non-transitory, computer-readable medium storinginstructions that, when executed, cause the one or more processors to:determine, based on pre-operative image data, one or morepatient-specific anatomical measurements associated with an anatomicaljoint of a patient, apply a statistical model of joint performance tothe one or more patient-specific anatomical measurements, wherein thestatistical model uses one or more transfer functions, identify one ormore cut angles for performing a surgical procedure based on the appliedstatistical model, generate a model of a patient-specific cutting guidebased on the one or more cut angles, and output the model to acomputer-readable storage device.
 17. The system of claim 16, whereinthe one or more patient-specific anatomical measurements comprise adistal condyle radius and an anterior condyle radius.
 18. The system ofclaim 16, wherein the instructions that cause the one or more processorsto identify one or more cut angles comprise instructions that, whenexecuted, cause the one or more processors to select a set of cut anglesthat substantially balance condylar gaps of the anatomical jointthroughout a range of motion associated with one or more physicalactivities.
 19. The system of claim 16, where the instructions thatcause the one or more processors to determine one or morepatient-specific anatomical measurements comprise instructions that,when executed, cause the one or more processors to: receive one or morepre-operative images depicting the anatomical joint; create athree-dimensional anatomical model of the anatomical joint based on theone or more pre-operative images; and determine the one or morepatient-specific anatomical measurements based on the three-dimensionalanatomical model.
 20. The system of claim 16, wherein the instructionsthat cause the one or more processors to apply a statistical model ofjoint performance comprise instructions that, when executed, cause theone or more processors to apply a Monte Carlo method to iterativelyevaluate a plurality of possible cut angles.
 21. The system of claim 20,wherein each iteration of the Monte Carlo method comprises applying theone or more transfer functions with a distinct set of parameters. 22.The system of claim 16, wherein the statistical model of jointperformance is a machine learning model.
 23. The system of claim 16,wherein the instructions that cause the one or more processors togenerate a model of a patient-specific cutting guide compriseinstructions that, when executed, cause the one or more processors tomodify a model of a cutting guide blank to include the one or moreapertures based on the one or more cut angles.
 24. The system of claim16, wherein the computer-readable storage device is in operablecommunication with a manufacturing device configured to manufacture thepatient-specific cutting guide.
 25. A system for creating anactivity-optimized cutting guide for a surgical procedure, the systemcomprising: one or more processors; and a non-transitory,computer-readable medium storing instructions that, when executed, causethe one or more processors to: determine, based on pre-operative imagedata, one or more patient-specific anatomical measurements associatedwith an anatomical joint of a patient, apply a statistical model ofjoint performance to the one or more patient-specific anatomicalmeasurements, wherein the statistical model is based on simulation datafor a plurality of joint geometries associated with a plurality ofimplants, identify one or more cut angles for performing a surgicalprocedure based on the applied statistical model, generate a model of apatient-specific cutting guide based on the one or more cut angles, andoutput the model to a computer-readable storage device.
 26. The systemof claim 25, wherein the one or more patient-specific anatomicalmeasurements comprise a distal condyle radius and an anterior condyleradius.
 27. The system of claim 25, wherein the instructions that causethe one or more processors to identify one or more cut angles compriseinstructions that, when executed, cause the one or more processors toselect a set of cut angles that substantially balance condylar gaps ofthe anatomical joint throughout a range of motion associated with one ormore physical activities.
 28. The system of claim 25, where theinstructions that cause the one or more processors to determine one ormore patient-specific anatomical measurements comprise instructionsthat, when executed, cause the one or more processors to: receive one ormore pre-operative images depicting the anatomical joint; create athree-dimensional anatomical model of the anatomical joint based on theone or more pre-operative images; and determine the one or morepatient-specific anatomical measurements based on the three-dimensionalanatomical model.
 29. The system of claim 25, wherein the instructionsthat cause the one or more processors to apply a statistical model ofjoint performance comprise instructions that, when executed, cause theone or more processors to apply a Monte Carlo method to iterativelyevaluate a plurality of possible cut angles.
 30. The system of claim 29,wherein each iteration of the Monte Carlo method comprises applying oneor more transfer functions with a distinct set of parameters.
 31. Thesystem of claim 25, wherein the statistical model of joint performanceis a machine learning model.
 32. The system of claim 25, wherein theinstructions that cause the one or more processors to generate a modelof a patient-specific cutting guide comprise instructions that, whenexecuted, cause the one or more processors to modify a model of acutting guide blank to include the one or more apertures based on theone or more cut angles.
 33. The system of claim 25, wherein thecomputer-readable storage device is in operable communication with amanufacturing device configured to manufacture the patient-specificcutting guide.
 34. The system of claim 25, wherein each implant of theplurality of implants is defined by a distinct set of implantationfeatures comprising one or more of an implant position, an implantorientation, and an implant type.
 35. The system of claim 25, whereinthe instructions, when executed, further cause the one or moreprocessors to generate the statistical model by: selecting the pluralityof implants; populating a database with the simulation data bysimulating a motion of the plurality of joint geometries whileperforming one or more physical activities and an expected stress on theplurality of implants resulting from the motion; and generating thestatistical model of joint performance based on the simulation data.